JP2003234901A - Image processing apparatus and image processing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing apparatus and an image processing method, capable of easily adjusting the contrast of a photographed image by using only the photographed image having a contrast difference portion and eliminating dependence on the input image and unbalanced color after conversion. <P>SOLUTION: The apparatus has an image input means 10 for converting an inputted analog image signal vi into digital image data, an image processing means 11 for applying desired processing to the converted image data to generate processed image data v, an image compositing means 12 for compositing the processed image data v with the digital image data to generate composited image data, and an image output means 13 for converting the composited image data into an analog composited signal. The means 11 is a contrast-adjusting means or a color-adjusting means. <P>COPYRIGHT: (C)2003,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image processing apparatus and an image processing apparatus capable of automatically adjusting the contrast or color of a color image signal obtained by a camera or the like to obtain an image having a desired contrast or color tone. It relates to a processing method.

[0002]

2. Description of the Related Art A color image photographed by a digital camera is affected by the SN level representing the noise ratio in the analog value obtained by a CCD element which is an image pickup element and the conversion accuracy when converting the analog value into a digital value. However, since it is limited to a range narrower than the dynamic range of the pixel density of a natural image actually photographed, there is a tendency that a phenomenon that information in a shaded detail is lost. This tendency is particularly large when an image of a sample in which a bright area and a dark area are mixed in an image is to be captured. As an improvement, a method of performing contrast enhancement so as to expand the range of the brightness and the like of a digital image from an image part having higher brightness to an image part having lower brightness can be first considered. The conventional method of contrast enhancement is to create a histogram showing the distribution of the brightness values of all pixels that make up the original image, and use the cumulative curve of the histogram as the brightness conversion curve to set the brightness values of the pixels in the original image to the new brightness. There is a histogram equalization method that converts into values and emphasizes the contrast of the image. In this method, since the luminance of pixels in the entire area of the original image is converted to new luminance by the same luminance conversion curve, there may be a part where the contrast is rather lowered. Therefore, when it is desired to perform the contrast enhancement over the entire image, it is necessary to perform the contrast enhancement processing suitable for the area.

As a further improvement of this method, many local histogram equalization methods have been proposed in which an image is divided into a plurality of rectangular areas and the histogram equalization method is applied to each area (for example, Japanese Patent Laid-Open No. 2000-2000). -2852
28, reference 1), and its configuration is as shown in FIG.

FIG. 28 is a block diagram showing an image processing apparatus in Document 1.

FIG. 28 shows a contrast emphasizing section, which is an image data dividing means 28 for dividing an image into rectangles.
1. Histogram creation means 282 for creating a histogram for each rectangle, and contrast expansion means 283 for expanding the contrast for each rectangle. However, when this method is used, problems have been pointed out that a rectangular area in which contrast is overemphasized may occur, or the contrast may become discontinuous at the boundary between adjacent rectangular areas.

On the other hand, as a solution to such a problem without using the histogram, the shutter time and the aperture of the digital camera are changed for each field, and the bright part and the dark part are separately imaged, and the obtained respective parts are obtained. A method has also been proposed in which information is combined into one image to realize a halftone density, thereby bringing the halftone density closer to the dynamic range of the pixel density of an actually photographed natural image. As an example thereof, there is an example referred to in Japanese Unexamined Patent Publication No. 6-141229 (reference 2), and the configuration diagram of the device is as shown in FIG.

FIG. 29 is a block diagram showing the image pickup device in Document 2.

In FIG. 29, 290 is an image pickup device (CCD) for performing a photoelectric effect on an object, 291 is a memory for recording an image signal, 292 is multiplication means for multiplying the signal level by a constant number, and 293 and 298 are image signal levels. Level weighting means for adding a weight accordingly, 294 is addition means for adding signals, 295 is speed conversion means for converting the speed of the image signal, 296 is level compression means for compressing the level of the image signal, and 297 is for each block. It is a timing control means for controlling the timing. This device performs weighted synthesis according to the signal levels of two or more images with different charge accumulation periods in the image sensor, converts the obtained synthesized image output to the speed of a standard television signal, and also uses it as a reference for the television signal. Since the present invention relates to a television image pickup apparatus that compresses to a level, it has a speed conversion means, a level compression means, and the like. Therefore, when applied to a digital camera, the speed conversion means and the level compression means are not necessary components. However, in the case of the method of combining images obtained in a plurality of charge accumulation periods like this device, the discontinuity of the contrast in the combined images is unlikely to occur, but at least two images are continuously taken. Cannot take the same image. Therefore, when these images are combined, there is a possibility that an image in which the details of the combined image are blurred or deviated although affected by the shutter speed. Also, if the density range for shooting a bright part and the density range for shooting a dark part do not cover the entire density range of the image, there is a risk of discontinuity between the two intermediate density ranges. is there.

By the way, when a human observes the details and colors in such a shaded area, the human visual senses the dynamics and colors of a wide density of an image without causing the above problems. Can be perceived. The concept of central vision / peripheral vision Retinex centered on human vision is described by Edwin Land (“An Alternative T.
technique for the Computat
ion of the Designator int
he Retinex Theory of Colo
r Vision ”National Academia
of Science, Vol. 84, pp. 3078 to pp. 3080 (1986)). In this, the concept of human visual Retinex is described by an inverse square function in which the central visual field has a diameter of 2 to 4 basic units and the peripheral visual field has a diameter of approximately 200 to 250 times the central visual field. ing. The spatial average of signal intensities in the central visual field and the peripheral visual field is defined as being related to the perceived intensity. According to these principles, a method for improving the color and lightness expression in the dark area as described above has been proposed in recent years. This example is shown in International Publication No. W097 / 45809 (In Japanese Patent Publication 2000-511
No. 315, reference 3).

FIG. 30 is an explanatory diagram of a digital image improving method in Document 3. Although a grayscale image is described here as an example, it can be extended to a color image. Pixel value I at (i, j) in the image
(I, j) is adjusted by the processor 301 and the filter 302, and filtering processing is performed. For each pixel, the processor 301 calculates an adjusted pixel value I ′ (i, j) as in (Equation 1).

[0011]

[Equation 1]

Here, F (i, j) is a peripheral visual field function representing the peripheral visual field, and "*" indicates the convolution calculation processing.
Then, the normalization coefficient K is determined so that F (i, j) satisfies the condition of (Equation 2).
The second term of is equivalent to the average value of the pixel values in the peripheral visual field.

[0013]

[Equation 2]

That is, (Equation 1) corresponds to a logarithmic conversion of the ratio of the pixel value of each pixel to the average pixel value in a large area. The peripheral visual field function F (i, j) is designed to have a higher contribution rate as it approaches the target pixel from the correspondence with the human visual model, and a Gaussian function as in (Equation 3) is applied.

[0015]

[Equation 3]

Here, c is a constant for controlling the adjusted pixel value I '(i, j) of each pixel value I (i, j).

As described above, in the digital image improving method of Document 3, the target pixel value with respect to the average pixel value in the peripheral visual field is calculated as the adjusted pixel value I '(i, j), and with respect to this value. , 302 performs filtering to produce the Retinex output R (i, j) used by display 303. 302 is I '(i,
j) is treated in the display 303 from the logarithmic domain.
The pixel value area is converted to the (i, j) pixel value area, and the processing of applying the same offset and gain conversion function to all the pixels is used for simplification of the processing.

However, this method is greatly affected by c controlling the peripheral visual field function. For example,
When the value of c is large, the peripheral visual field that contributes to the target pixel is large, so that only the color compensation in a large shadow is possible. On the other hand, when the value of c is small, only the vicinity of the target pixel is affected. It will be given and only improvement in small shadow areas is seen. It is necessary to consider appropriate c according to the dynamic range of pixel values in the image treated in this way, and there is a problem of image dependence. As an improvement, a method of providing a plurality of peripheral vision areas is also proposed in the same patent, but it is not clear how many peripheral vision areas are prepared, and a large peripheral area is required to improve the improvement accuracy. Therefore, there is a problem in that the processing time becomes huge by preparing many small peripheral areas.

Next, in the filter 302, a process of converting into an actual pixel value used in the display 303 is performed. In this method, the same offset and gain conversion function process is performed on any image. As described above, there is a problem that empirical knowledge is required for setting the optimum offset and gain conversion functions. Furthermore, when the pixel value variation within the maximum peripheral visual field set by the plurality of constants c is very small, the adjusted pixel value I ′
Even if a plurality of regions are prepared, (i, j) will be around 1.0 regardless of I (i, j). In such a case, I ′ (i, j) at the target pixel with a small pixel value variation is often located near the average of I ′ (i, j) in the entire input image, and the offset and gain in the filter 302 are increased. Regardless of the conversion function, there is a tendency to move toward the center of the actual pixel value. In particular, in a uniformly wide image area having highlight brightness, there is a problem that the brightness after adjustment is easily adjusted in the direction of decreasing and it is visually deteriorated.

On the other hand, when the color adjustment processing is used as the image processing, in the conventional color adjustment processing, a method of previously defining a color before conversion and a color after conversion is used in the color conversion table. If you want to make the image taken by the camera more beautiful, the color before conversion is often not known until you look at the input image, and it is not possible to uniformly decide in advance and color adjustment can be performed automatically. There is a problem that you cannot do it. To improve this, a method of taking a color histogram in the input image and converting the upper color with the post-conversion color in the color conversion table can be considered, but simply taking the color histogram will result in a color balance after conversion. There was a high possibility that it would collapse.

[0021]

As described above, the conventional histogram-based contrast enhancement processing technique is such that the contrast of a specific portion is over-emphasized or a discontinuity is exhibited at a locally adjacent rectangular region boundary. There was a problem that there is. In addition, in the case where a plurality of images captured under a plurality of aperture conditions and the like are combined, in principle, the same subject cannot be captured, which may result in blurring of details in the combined image and color shift. Had. Furthermore, with conventional contrast adjustment technology based on the human visual model, experience in designing constants for defining the human peripheral visual field and filter processing when converting to the actual pixel value to be finally handled. It had a problem that it included a lot of intellectual knowledge. In addition, particularly in a uniformly wide image area having highlight brightness, there is a problem in that the brightness after adjustment is easily adjusted in the direction of decreasing and the deterioration is visually caused.

With this image processing apparatus and image processing method, it is possible to easily adjust the contrast of a picked-up image by using only the picked-up image having bright and dark parts, and to determine the dependence on the input image and the color balance after conversion. It is required that the collapse can be eliminated.

In order to meet this requirement, the present invention can easily adjust the contrast of a picked-up image by using only the picked-up image having bright and dark parts, and also can show the dependency on the input image and the color balance after conversion. The image processing device that can eliminate the collapse of the image and the contrast of the captured image can be easily adjusted by using only the image having the captured bright and dark parts, and the dependency on the input image and the collapse of the color balance after conversion can be prevented. An object is to provide an image processing method for eliminating the loss.

[0024]

In order to solve the above-mentioned problems, the image processing apparatus of the present invention comprises an image input means for converting an input analog image signal into digital image data and a desired image in the converted digital image data. Image processing means for performing processing to generate processed image data, image synthesizing means for synthesizing processed image data and digital image data to generate synthetic image data, and synthetic image data for analog synthetic image signal And an image output unit for converting the image into the image processing unit. .

With this, it is possible to easily adjust the contrast of the picked-up image by using only the picked-up image having the bright and dark portions, and to eliminate the dependency on the input image and the collapse of the color balance after conversion. An image processing device is obtained.

In order to solve the above problems, the image processing method of the present invention has an image input step of converting an input analog image signal into digital image data, and a desired image processing is performed on the converted digital image data to complete the processing. An image processing step of generating image data, an image synthesizing step of synthesizing the processed image data and digital image data to generate synthetic image data, and an image outputting step of converting the synthetic image data into an analog synthetic image signal. And the image processing step is configured to be a contrast adjustment step for performing contrast adjustment on the digital image data or a color adjustment step for performing color adjustment on the digital image data.

Thus, the contrast of the picked-up image can be easily adjusted using only the picked-up image having the bright and dark portions,
Further, an image processing method for eliminating the dependency on the input image and the loss of color balance after conversion can be obtained.

[0028]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An image processing apparatus according to claim 1 of the present invention includes image input means for converting an input analog image signal into digital image data and desired image processing for the converted digital image data. Image processing means for generating processed image data, image combining means for generating processed image data by combining the processed image data and digital image data, and converting the combined image data into an analog combined image signal. The image processing means has an image output means, and the image processing means is a contrast adjusting means for performing contrast adjustment on the digital image data or a color adjusting means for performing color adjustment on the digital image data.

With this configuration, no experience is required and
Since contrast adjustment or color adjustment can be performed on the input image, it is possible to easily adjust the contrast of the captured image using only the captured image with bright and dark parts, and the dependence and conversion on the input image. It has the effect of eliminating the later loss of color balance.

According to a second aspect of the present invention, in the image processing apparatus according to the first aspect, the contrast adjusting means includes correction information deriving means for obtaining a contrast adjustment amount of the target pixel, and the obtained contrast of the target pixel. And a pixel value conversion unit that converts the pixel value of the target pixel into an adjusted pixel value in the limited range based on the contrast adjustment amount of the target pixel. It is what

With this configuration, the contrast adjustment amount of the target pixel is obtained, and the pixel value of the target pixel is converted into the adjusted pixel value within a limited range based on the contrast adjustment amount of the target pixel, so that experience is required. In this case, the contrast adjustment or the color adjustment can be automatically and reliably performed on the input image.

An image processing apparatus according to a third aspect is the image processing apparatus according to the first aspect, wherein the contrast adjusting means sets an initial condition and a pixel comparison range when calculating the contrast adjustment amount of the target pixel. Initial setting means, correction information derivation means for obtaining the contrast adjustment amount of the target pixel based on the pixel comparison range, and whether the contrast adjustment processing based on the obtained contrast adjustment amount of the target pixel has been completed in all pixel comparison ranges An end determining unit that determines whether or not it is determined, a correction range changing unit that changes the pixel comparison range and passes the processing to the correction information deriving unit when the end determining unit does not determine the end, and the end determining unit determines the end. If the contrast adjustment amount obtained from a plurality of pixel comparison ranges is limited, the effective range is extracted and the contrast of the target pixel is extracted. In which it was decided to have the pixel value converting means for converting the pixel value of the target pixel to the adjusted pixel values at the limited range based on the adjustment amount.

With this configuration, the contrast adjustment amount calculated by comparing the target pixel value with the average pixel value in the peripheral region can be expanded to the weighted average value of the contrast adjustment amounts in the plurality of peripheral regions. This has the effect of reducing the influence of the image and the influence of setting a constant indicating the size of the peripheral visual field.

An image processing apparatus according to a fourth aspect is the image processing apparatus according to the first aspect, wherein the contrast adjusting means converts a pixel value in the input image into a target signal to be a target for contrast adjustment. Effective from the conversion unit, the target correction information derivation unit that obtains the contrast adjustment amount of the target pixel with respect to the target signal obtained by the signal conversion unit, and the contrast adjustment amount of the target pixel obtained by the target correction information derivation unit. Extraction means for limiting and extracting a range, and a target signal for generating an adjusted target signal by converting the target signal obtained by the signal converting means into an adjustment target signal in a limited range based on the contrast adjustment amount of the target pixel A signal for inversely converting the pixel value in the input image into an adjusted pixel value by the conversion means, the adjusted target signal, and the target signal obtained by the signal conversion means. In which it was decided to have the converting means.

With this configuration, the input image is once converted into information such as lightness and hue, and the lightness contrast adjustment amount is obtained by comparing the lightness of the target pixel with the average lightness in the peripheral region using this lightness as a target signal. It is possible to extract the contrast adjustment amount of the lightness that seems to be effective, so the pixel value of the adjusted image is calculated from the extracted lightness and the hue obtained from the input image, and the obtained contrast adjustment is obtained. There is an effect that the contrast of the input image can be adjusted by performing the weighted average synthesis of the image and the input image based on an appropriate coupling coefficient.

An image processing apparatus according to a fifth aspect is the image processing apparatus according to the first aspect, wherein the contrast adjusting means converts the pixel value in the input image into a target signal to be a target for contrast adjustment. A conversion unit, an initial setting unit that sets an initial condition and a pixel comparison range when calculating the contrast adjustment amount for the converted target signal, and a contrast adjustment amount of each pixel for the target signal converted based on the pixel comparison range. The target correction information derivation means for determining, the end determination means for determining whether or not the contrast adjustment processing based on the obtained contrast adjustment amount of each pixel is completed in all pixel comparison ranges, and the end determination means does not determine the end. If it is determined that the pixel comparison range is changed, the end is determined by the correction range changing unit that passes the process to the target correction information deriving unit and the end determining unit. Extraction means for extracting as limiting the scope of the effective than the contrast adjustment amount of each pixel in the case,
Target signal converting means for converting the target signal converted in a limited range based on the contrast adjustment amount of each pixel into an adjusted target signal to generate an adjusted target signal, and the adjusted target signal and the signal converting means. The signal inversion means for inversely converting the pixel value in the input image into the adjusted pixel value according to the target signal.

With this configuration, the contrast adjustment amount calculated by comparing the target pixel value with the average pixel value in the peripheral region can be expanded to the weighted average value of the contrast adjustment amounts in the plurality of peripheral regions. This has the effect of enabling accurate contrast adjustment.

An image processing apparatus according to a sixth aspect is the image processing apparatus according to the second or third aspect, wherein the pixel value conversion means calculates an average luminance calculation means for calculating an average luminance in the input image. Based on the average brightness, the conversion method classification means for selecting the conversion method when converting the pixel value in the input image into the adjusted pixel value based on the obtained contrast adjustment amount, and the conversion method classification means According to the conversion method, a pixel value estimating means for converting the obtained contrast adjustment amount into a pixel value of an actual pixel is provided.

With this configuration, after converting a region considered to be effective from the contrast adjustment amount obtained by comparison with the surrounding pixels, a conversion for converting the extracted contrast adjustment amount based on the average brightness in the input image. Since a method can be selected and applied, there is an effect that contrast adjustment can be performed easily and reliably.

According to a seventh aspect of the present invention, in the image processing apparatus according to the fourth or fifth aspect, the target signal converting means calculates an average value of the target signals obtained by the signal converting means in the input image. Then, based on the average target signal calculation means for generating the average target signal and the generated average target signal,
According to the conversion method obtained by the conversion method classification means and the conversion method classification means for selecting the conversion method when converting the target signal obtained based on the contrast adjustment amount of the obtained target signal to the adjustment target signal, The target signal estimating means for converting the contrast adjustment amount of the target signal thus obtained into the target signal value of the actual pixel.

With this configuration, after extracting an area that seems to be effective from the contrast adjustment amount of the target signal (for example, brightness) obtained by comparison with the surrounding pixels, the average target signal value (average brightness) in the input image is also calculated. In addition, since a conversion method for converting the contrast adjustment amount of the extracted target signal can be selected and applied, it has an effect that the contrast adjustment can be performed easily and reliably.

An image processing apparatus according to claim 8 is the image processing apparatus according to claim 2 or 3, wherein the pixel value converting means calculates a reference intensity value indicating a contrast intensity in the input image. And a conversion curve estimating means for estimating a conversion curve when converting a pixel value in the input image into an adjusted pixel value based on the obtained contrast adjustment amount based on the calculated reference intensity value. Pixel value estimating means for converting the obtained contrast adjustment amount into a pixel value of an actual pixel using a conversion curve.

With this configuration, the sum of the edge intensities obtained from the brightness of the input image can be calculated after extracting an area that seems to be effective from the contrast adjustment amount obtained by comparison with the surrounding pixels. Prepare multiple conversion curves when converting the contrast adjustment amount
It is possible to estimate the optimum conversion curve with a genetic algorithm by calculating the edge strength sum of the brightness of the final output candidate image obtained from each conversion curve and using the ratio of that value to the edge strength sum of the input image as an evaluation value. Has the effect of.

According to a ninth aspect of the present invention, in the image processing apparatus according to the fourth or fifth aspect, the target signal converting means has a contrast in the input image with respect to the target signal obtained by the signal converting means. Based on the reference strength calculation means for calculating the reference strength value indicating the strength, and the calculated reference strength value,
The target signal conversion curve estimating means for estimating the conversion curve when converting the target signal obtained based on the contrast adjustment amount of the obtained target signal into the adjustment target signal, and the estimated conversion curve And a target signal estimating means for converting the target signal into an adjustment target signal.

With this configuration, after extracting a region that seems to be effective from the target signal (brightness) contrast adjustment amount obtained by comparison with the surrounding pixels, the sum of the edge strength of the input image is calculated from the target signal of the input image. Therefore, multiple conversion curves for converting the contrast adjustment amount of the extracted target signal are prepared, and the total edge strength of the target signal of the final output candidate image obtained from each conversion curve is calculated. , Has the effect that the optimum conversion curve can be estimated by the genetic algorithm using the ratio of that value to the total edge strength of the input image as an evaluation value.

An image processing apparatus according to a tenth aspect of the present invention is the image processing apparatus according to the eighth aspect, wherein the conversion curve estimating means is an initial candidate of an adjustment vector composed of a preset fixed number of adjustment parameters. Pixel value conversion candidate calculation for converting the pixel value in the input image into the adjusted pixel value based on the obtained contrast adjustment amount using the initial candidate setting means for setting the group and each vector in the adjustment vector group at the present time Means, an evaluation value calculating means for evaluating the contrast strength by each conversion curve candidate using the converted pixel value obtained from the reference intensity value and each vector in the adjustment vector group at the present time, and the previous term evaluation value deriving means The fitness calculation means for calculating the goodness of fit of each candidate obtained in step 1, and a new adjustment is performed by performing a recombination operation of the current candidate based on the goodness of fit of each candidate calculated. In which it was decided to have an estimation end judgment means for performing an operation unit reclassified for generating a vector set, a determination of whether the time optimization of the adjustment vector is completed.

With this configuration, the sum of the edge intensities obtained from the brightness of the input image can be calculated after extracting the region which is considered to be more effective than the contrast adjustment amount obtained by comparison with the surrounding pixels, and is therefore extracted. Prepare a plurality of conversion curves for converting the contrast adjustment amount, calculate the total edge strength of the luminance of the final output candidate image obtained from each conversion curve, and calculate the ratio of that value to the total edge strength of the input image. Has an effect that the optimum conversion curve can be estimated by a genetic algorithm using as an evaluation value.

An image processing apparatus according to an eleventh aspect is the image processing apparatus according to the ninth aspect, wherein the target signal conversion curve estimation means is an adjustment vector composed of a preset fixed number of adjustment parameters. Using the initial candidate setting means for setting the initial candidate group and each vector in the adjustment vector group at the present time, the obtained target signal is converted into the adjustment target signal based on the contrast adjustment amount of the obtained target signal. Evaluation for evaluating the contrast strength of the target signal by each conversion curve candidate using the target signal conversion candidate calculation means and the target signal value after conversion obtained from the reference intensity value and each vector in the adjustment vector group at the present time A value calculation means, a goodness-of-fit calculation means for calculating the goodness-of-fit of each candidate obtained by the evaluation-value deriving means, and a current condition based on the calculated goodness-of-fit of each candidate. And recombinant recombinant for generating new adjustment vector collection by performing an operation manipulation means, in which the optimization of the adjustment vector was to have the estimated completion determining means for determining whether the time has ended.

With this configuration, after extracting a region considered effective from the target signal (brightness) contrast adjustment amount obtained by comparison with the surrounding pixels, the sum of the edge strength of the input image is calculated from the target signal of the input image. Therefore, multiple conversion curves for converting the contrast adjustment amount of the extracted target signal are prepared, and the total edge strength of the target signal of the final output candidate image obtained from each conversion curve is calculated. , Has the effect that the optimum conversion curve can be estimated by the genetic algorithm using the ratio of the value to the total edge strength of the input image as an evaluation value.

According to a twelfth aspect of the present invention, in the image processing apparatus according to the first aspect, the color adjusting means obtains the image dividing means for dividing the input image into a plurality of small areas and the image dividing means. Representative color extraction means for obtaining a representative color representative of the color data in each of the small areas, a color conversion table prepared in advance for collecting color data after color conversion, and a representative color obtained by the representative color extraction means. Based on the representative color conversion color selecting means for selecting the closest color from the color conversion table as the conversion color and the distance between the representative color and the conversion color obtained by the representative color conversion color selecting means, each selected color is selected. The color conversion means finely adjusts the converted color and converts the representative color into the converted color after the fine adjustment.

With this configuration, the input image can be divided into a plurality of detailed areas, and the representative color can be selected based on the statistical distribution of the colors in each area. Therefore, the representative color and the representative color in the color conversion table can be selected. The colors are compared to extract the converted color closest to each representative color, and the selected color data after conversion is finely adjusted based on the distance between the two colors to determine the representative color in each area. It is possible to convert to this finely adjusted conversion color, and to perform color adjustment of the input image by performing weighted average synthesis of the image after color adjustment thus obtained and the input image based on an appropriate coupling coefficient. It has the effect that

An image processing apparatus according to a thirteenth aspect is the image processing apparatus according to the twelfth aspect, wherein the representative color extracting means performs sequential division processing on the color data in the divided small areas. Set a group in the start state, classify all the color data into each group, and divide the target group based on the initialization method that obtains the representative color of each group and the distribution of the color data that belongs to the division target group. Sometimes a division axis determining means for determining the component of interest, a cluster dividing means for dividing the target group into a plurality of groups according to the obtained focus component, and for allocating color data belonging to the target group to the groups obtained after the division are obtained. Cluster representative color determination means for obtaining a representative color of the color data belonging to each of the groups, and whether or not the representative colors have converged. If the convergence determination means determines that the processing has moved to the cluster division means for performing the group division again based on the current representative color, and the convergence determination means determines that the target area has been obtained so far. It is determined whether or not a predetermined number of representative colors from are obtained, and when not obtained, the cluster division end determination means to which the processing to the division axis determination means shifts, and the cluster division end determination means is determined to have ended. In this case, a representative color output means for outputting the obtained predetermined number of representative colors is provided.

With this configuration, the input image can be divided into a plurality of detail areas, and the representative color can be selected based on the statistical distribution of the colors in each area. Therefore, the representative color and the representative color in the color conversion table can be selected. The colors are compared to extract the converted color closest to each representative color, and the selected color data after conversion is finely adjusted based on the distance between the two colors to determine the representative color in each area. It is possible to convert to this finely adjusted conversion color, and to perform color adjustment of the input image by performing weighted average synthesis of the image after color adjustment thus obtained and the input image based on an appropriate coupling coefficient. It has the effect of being able to

An image processing apparatus according to a fourteenth aspect is the image processing apparatus according to any one of the first to thirteenth aspects, wherein the image synthesizing means is the input image and the adjusted image obtained by the image processing means. And a selection reference value determining means for determining which is to be prioritized, and a coupling coefficient to be applied to the input image and the adjusted image obtained by the image processing means is determined based on the determination result of the selection reference value determining means. Using the coupling coefficient deriving means and the coupling coefficient of each image determined by the coupling coefficient deriving means,
A weighted average synthesizing unit for generating a weighted average image of the input image and the adjusted image obtained by the image processing unit is provided.

With this configuration, the contrast adjustment amount can be obtained by comparing the pixel value of the target pixel with the average pixel value in the peripheral region thereof, and the effective contrast adjustment amount can be extracted from that value. The contrast of the input image can be adjusted by performing the weighted average synthesis of the obtained contrast adjusted image and the input image based on an appropriate coupling coefficient.

According to the fifteenth aspect of the present invention, in the image processing method, an image input step of converting an input analog image signal into digital image data and a desired image processing of the converted digital image data to generate processed image data. An image processing step of performing processing, a processed image data and digital image data are combined to generate combined image data, and an image output step of converting the combined image data into an analog combined image signal. The image processing step is a contrast adjustment step for performing contrast adjustment on the digital image data or a color adjustment step for performing color adjustment on the digital image data.

With this configuration, no experience is required and
Since contrast adjustment or color adjustment can be performed on the input image, it is possible to easily adjust the contrast of the captured image using only the captured image with bright and dark parts, and the dependence and conversion on the input image. It has the effect of eliminating the later loss of color balance.

According to a sixteenth aspect of the present invention, in the image processing method according to the fifteenth aspect, the contrast adjustment step includes a step of deriving correction information for obtaining a contrast adjustment amount of the target pixel and a contrast of the obtained target pixel. An extraction step of limiting the effective range from the adjustment amount and a pixel value conversion step of converting the pixel value of the target pixel into the adjusted pixel value in the limited range based on the contrast adjustment amount of the target pixel. It is what

With this configuration, the contrast adjustment amount of the target pixel is obtained, and the pixel value of the target pixel is converted into the adjusted pixel value within a limited range based on the contrast adjustment amount of the target pixel. Therefore, experience is required. There is no need to adjust the contrast of the input image automatically and reliably.

An image processing method according to a seventeenth aspect is the image processing method according to the fifteenth aspect, wherein the contrast adjusting step sets an initial condition and a pixel comparison range for calculating the contrast adjusting amount of the target pixel. Whether the initial setting step, the correction information derivation step for obtaining the contrast adjustment amount of the target pixel based on the pixel comparison range, and the contrast adjustment processing based on the obtained contrast adjustment amount of the target pixel are completed in all pixel comparison ranges An end determination step for determining whether or not it is performed, a correction range changing step for changing the pixel comparison range and passing the process to the correction information derivation step if the end determination step is not finished, and an end determination step is performed. If it is, the effective range is limited and extracted from the contrast adjustment amount obtained from multiple pixel comparison ranges. An extraction step that is obtained by the fact that having a pixel value conversion step of converting the pixel value of the target pixel to the adjusted pixel values at the limited range based on the contrast adjustment amount of the target pixel.

With this configuration, the contrast adjustment amount calculated by comparing the target pixel value with the average pixel value in the peripheral region can be expanded to the weighted average value of the contrast adjustment amounts in the plurality of peripheral regions. This has the effect of reducing the influence of the image and the influence of setting a constant indicating the size of the peripheral visual field.

The image processing method according to the eighteenth aspect is the image processing method according to the fifteenth aspect, wherein the contrast adjusting step is a signal for converting a pixel value in the input image into a target signal to be a target for contrast adjustment. Effective from the conversion step, the target correction information derivation step of obtaining the contrast adjustment amount of the target pixel for the target signal obtained in the signal conversion step, and the contrast adjustment amount of the target pixel obtained in the target correction information derivation step. An extraction step for limiting and extracting the range, and a target signal for generating an adjusted target signal by converting the target signal obtained in the signal conversion step into the adjustment target signal in the limited range based on the contrast adjustment amount of the target pixel The conversion step, the adjusted target signal and the target signal obtained in the signal conversion step Pixel value is obtained by the fact that and a signal inverse conversion step of performing an inverse transform process to adjust pixel values.

With this configuration, the input image is once converted into information such as lightness and hue, and the lightness contrast adjustment amount is obtained by comparing the lightness of the target pixel with the average lightness in the peripheral region using this lightness as a target signal. It is possible to extract the contrast adjustment amount of the lightness that seems to be effective, so the pixel value of the adjusted image is calculated from the extracted lightness and the hue obtained from the input image, and the obtained contrast adjustment is obtained. There is an effect that the contrast of the input image can be adjusted by performing the weighted average synthesis of the image and the input image based on an appropriate coupling coefficient.

An image processing method according to a nineteenth aspect is the image processing method according to the fifteenth aspect, wherein the contrast adjusting step converts a pixel value in the input image into a target signal to be a target for contrast adjustment. A conversion step, an initial setting step for setting an initial condition and a pixel comparison range when calculating the contrast adjustment amount for the converted target signal, and a contrast adjustment amount of each pixel for the converted target signal based on the pixel comparison range A target correction information derivation step for obtaining, and an end determination step for determining whether or not the contrast adjustment processing based on the obtained contrast adjustment amount of each pixel is completed in all pixel comparison ranges,
If the end determination step does not determine the end, the correction range changing step that changes the pixel comparison range and passes the process to the target correction information deriving step; and if the end determination step determines the end, contrast adjustment of each pixel The extraction step of limiting the range that is more effective than the amount, and the target of converting the target signal converted in the limited range based on the contrast adjustment amount of each pixel to the adjustment target signal to generate the adjusted target signal. A signal conversion step and a signal inverse conversion step of performing inverse conversion processing of a pixel value in the input image into an adjusted pixel value by the adjusted target signal and the target signal converted in the signal conversion step are provided. .

With this configuration, the contrast adjustment amount calculated by comparing the target pixel value with the average pixel value in the peripheral region can be expanded to the weighted average value of the contrast adjustment amounts in the plurality of peripheral regions. This has the effect of enabling accurate contrast adjustment.

An image processing method according to a twentieth aspect is the image processing method according to the sixteenth aspect or the seventeenth aspect, wherein the pixel value converting step is an average luminance calculating step for calculating an average luminance in the input image. Based on the average brightness, the conversion method classification step that selects the conversion method when converting the pixel value in the input image to the adjusted pixel value based on the obtained contrast adjustment amount, and the conversion method classification step A pixel value estimation step of converting the obtained contrast adjustment amount into a pixel value of an actual pixel according to the conversion method.

With this configuration, after converting an area that seems to be effective from the contrast adjustment amount obtained by comparison with the surrounding pixels, a conversion for converting the extracted contrast adjustment amount based on the average brightness in the input image. Since a method can be selected and applied, there is an effect that contrast adjustment can be performed easily and reliably.

The image processing method according to claim 21 is the image processing method according to claim 18 or 19, wherein the target signal converting step calculates an average value of the target signals obtained in the signal converting step in the input image. And an average target signal calculation step of generating an average target signal, and converting the target signal obtained based on the obtained target signal based on the contrast adjustment amount of the obtained target signal into an adjustment target signal. A conversion method classification step for selecting the conversion method of
According to the conversion method obtained in the conversion method classification step, a target signal estimation step of converting the obtained contrast adjustment amount of the target signal into an actual target signal value of a pixel.

With this configuration, after extracting a region considered effective from the contrast adjustment amount of the target signal (for example, brightness) obtained by comparison with the surrounding pixels, the average target signal value (average brightness) in the input image is also calculated. In addition, since a conversion method for converting the contrast adjustment amount of the extracted target signal can be selected and applied, there is an effect that the contrast adjustment can be performed easily and reliably.

An image processing method according to a twenty-second aspect is the image processing method according to the sixteenth aspect or the seventeenth aspect, wherein the pixel value conversion step calculates a reference intensity value indicating a contrast intensity in the input image. And a conversion curve estimation step for estimating a conversion curve when converting a pixel value in the input image into an adjusted pixel value based on the obtained contrast adjustment amount based on the calculated reference intensity value. And a pixel value estimation step of converting the obtained contrast adjustment amount into a pixel value of an actual pixel using a conversion curve.

With this configuration, the sum of the edge intensities obtained from the brightness of the input image can be calculated after extracting the region that seems to be effective from the contrast adjustment amount obtained by comparison with the surrounding pixels. Prepare multiple conversion curves when converting the contrast adjustment amount
It is possible to estimate the optimum conversion curve with a genetic algorithm by calculating the edge strength sum of the brightness of the final output candidate image obtained from each conversion curve and using the ratio of that value to the edge strength sum of the input image as an evaluation value. Has the effect of.

An image processing method according to a twenty-third aspect is the image processing method according to the eighteenth or nineteenth aspect, wherein the target signal conversion step is a contrast in the input image with respect to the target signal obtained in the signal conversion step. When converting the target signal obtained based on the contrast adjustment amount of the obtained target signal to the adjustment target signal, based on the reference intensity calculation step of calculating the reference intensity value indicating the intensity, and the calculated reference intensity value. The target signal conversion curve estimation step of estimating the conversion curve of 1) and the target signal estimation step of converting the obtained target signal into an adjustment target signal using the estimated conversion curve.

With this configuration, after extracting an area that seems to be effective from the target signal (brightness) contrast adjustment amount obtained by comparison with the surrounding pixels, the sum of the edge strength of the input image is calculated from the target signal of the input image. Therefore, multiple conversion curves for converting the contrast adjustment amount of the extracted target signal are prepared, and the total edge strength of the target signal of the final output candidate image obtained from each conversion curve is calculated. , Has the effect that the optimum conversion curve can be estimated by the genetic algorithm using the ratio of the value to the total edge strength of the input image as an evaluation value.

An image processing method according to a twenty-fourth aspect is the image processing method according to the twenty-second aspect, wherein the conversion curve estimating step is an initial candidate of an adjustment vector composed of a preset fixed number of adjustment parameters. Pixel value conversion candidate calculation that converts the pixel value in the input image into the adjusted pixel value based on the obtained contrast adjustment amount using the initial candidate setting step for setting the group and each vector in the adjustment vector group at the present time Step, an evaluation value calculation step for evaluating the contrast strength by each conversion curve candidate using the converted pixel value obtained from the reference intensity value and each vector in the adjustment vector group at the present time, and the previous evaluation value derivation step Goodness of fit calculation step for calculating the goodness of fit of each candidate obtained in, and recomposition of the current candidate based on the calculated goodness of fit of each candidate An operation step reclassified and generates the new adjustment vector collection by performing an operation, in which the optimization of the adjustment vector was to have an estimated completion determination step for determining whether the time has ended.

With this configuration, the sum of the edge intensities obtained from the brightness of the input image can be calculated after extracting the region that is considered to be more effective than the contrast adjustment amount obtained by comparison with the surrounding pixels, so that it is extracted. Prepare a plurality of conversion curves for converting the contrast adjustment amount, calculate the total edge strength of the luminance of the final output candidate image obtained from each conversion curve, and calculate the ratio of that value to the total edge strength of the input image. Has an effect that the optimum conversion curve can be estimated by a genetic algorithm using as an evaluation value.

The image processing method according to a twenty-fifth aspect is the image processing method according to the twenty-third aspect, wherein the target signal conversion curve estimation step is an adjustment vector composed of a preset fixed number of adjustment parameters. Using the initial candidate setting step of setting the initial candidate group and each vector in the adjustment vector group at the present time, the obtained target signal is converted into the adjustment target signal based on the contrast adjustment amount of the obtained target signal. Evaluation for evaluating the contrast strength of the target signal by each conversion curve candidate using the target signal conversion candidate calculation step and the target signal value after conversion obtained from each vector in the adjustment vector group at the present time and the reference intensity value Value calculation step, fitness calculation step that calculates the fitness of each candidate obtained in the evaluation value derivation step, and each calculated A recombination operation step for generating a new adjustment vector set by performing a recombination operation on the current candidate based on the goodness of fit, and an estimation end determination step for determining whether or not the optimization of the adjustment vector is finished. To have.

With this configuration, after extracting an area that is considered effective from the target signal (brightness) contrast adjustment amount obtained by comparison with the surrounding pixels, the sum of the edge strength of the input image is calculated from the target signal of the input image. Therefore, multiple conversion curves for converting the contrast adjustment amount of the extracted target signal are prepared, and the total edge strength of the target signal of the final output candidate image obtained from each conversion curve is calculated. , Has the effect that the optimum conversion curve can be estimated by the genetic algorithm using the ratio of the value to the total edge strength of the input image as an evaluation value.

An image processing method according to a twenty-sixth aspect is the image processing method according to the fifteenth aspect, wherein the color adjusting step includes an image dividing step of dividing the input image into a plurality of small areas and an image dividing step. A representative color extraction step for obtaining a representative color representative of the color data in each of the small areas, a color conversion table prepared in advance for collecting color data after color conversion, and a representative color obtained in the representative color extraction step. Based on the representative color conversion color selection step of selecting the closest color from the color conversion table as the conversion color and the distance between the representative color and the conversion color obtained in the representative color conversion color selection step, each selected color is selected. A color conversion step of finely adjusting the conversion color and converting the representative color into the conversion color after the fine adjustment.

With this configuration, the input image can be divided into a plurality of detail areas, and the representative color can be selected based on the statistical distribution of the colors in each area. Therefore, the representative color and the representative color in the color conversion table can be selected. The colors are compared to extract the converted color closest to each representative color, and the selected color data after conversion is finely adjusted based on the distance between the two colors to determine the representative color in each area. It is possible to convert to this finely adjusted conversion color, and to perform color adjustment of the input image by performing weighted average synthesis of the image after color adjustment thus obtained and the input image based on an appropriate coupling coefficient. It has the effect that

An image processing method according to a twenty-seventh aspect is the image processing method according to the twenty-sixth aspect, wherein the representative color extracting step is performed when the color data in the divided small areas are sequentially divided. Set the start state group of
Based on the initialization step of classifying all the color data into each group and obtaining the representative color of each group, and the distribution axis of the color data belonging to the division target group, the division axis that determines the component of interest when dividing the target group According to the determination step and the obtained target component, the target group is divided into a plurality of groups, and the cluster division step of allocating the color data belonging to the target group to the groups obtained after the division, and the obtained color data of each group A cluster representative color determination step for obtaining a representative color and a judgment as to whether or not the representative colors have converged. If the representative colors have not converged, go to the cluster division step to perform group division again based on the current representative color. If it is determined in the convergence determination step that the processing shifts and the convergence determination step, is it within the target area obtained so far? It is determined whether or not a predetermined number of representative colors have been obtained, and if not obtained, the cluster division end determination step in which the processing to the division axis determination step moves and the cluster division end determination step In this case, a representative color output step of outputting the obtained predetermined number of representative colors is provided.

With this configuration, the input image can be divided into a plurality of detailed areas, and the representative color can be selected based on the statistical distribution of the colors in each area. Therefore, the representative color and the color in the color conversion table prepared in advance can be selected. The colors are compared to extract the converted color closest to each representative color, and the selected color data after conversion is finely adjusted based on the distance between the two colors to determine the representative color in each area. It is possible to convert to this finely adjusted conversion color, and to perform color adjustment of the input image by performing weighted average synthesis of the image after color adjustment thus obtained and the input image based on an appropriate coupling coefficient. It has the effect that

An image processing method according to a twenty-eighth aspect is the image processing method according to any one of the fifteenth to twenty-seventh aspects, wherein the image synthesizing step is the input image and the adjusted image obtained in the image processing step. Based on the selection reference value determination step that determines which of the two is to be prioritized and the determination result of the selection reference value determination step, the coupling coefficient to be applied to the input image and the adjusted image obtained in the image processing step is determined. A combination coefficient derivation step and a weighted average synthesis step of generating a weighted average image of the input image and the adjusted image obtained in the image processing step using the combination coefficient of each image determined in the combination coefficient derivation step. I decided to have it.

With this configuration, the contrast adjustment amount can be obtained by comparing the pixel value of the target pixel with the average pixel value in the peripheral region, and the effective contrast adjustment amount can be extracted from that value. The contrast of the input image can be adjusted by performing the weighted average synthesis of the obtained contrast adjusted image and the input image based on an appropriate coupling coefficient.

It should be noted that, hereinafter, all pixel units are used as the unit of the pixel position (i, j). Further, the steps in each embodiment may be configured by hardware or software.

(Embodiment 1) FIG. 1 is a block diagram showing a basic configuration of an image processing apparatus according to Embodiment 1 of the present invention. 2A is a block diagram showing the image processing means of FIG. 1, FIG. 3 is a block diagram showing the contrast adjusting means of FIG. 2A, and FIG. 10 is a block diagram showing the image synthesizing means of FIG. FIG. 11 is an explanatory diagram schematically showing human vision.

In FIG. 1, vi is an analog input image, vo is an output image to be finally output, and v is a processed image obtained by the image processing means 11 described later. 10
Is an analog image signal vi from an image pickup device such as a CCD device
Image input means for converting the image into digital image data, 11
Is an image processing means for performing desired image processing on the digital image data obtained by the image input means 10, and 12 is the digital image data from the image input means and the processed digital image data v obtained by the image processing means 11. An image synthesizing means for synthesizing, 13 is an image output means for outputting the synthesized digital image data obtained by the image synthesizing means 12 to a desired device (printer, display, etc.) as an image (analog image signal) after final processing. is there. In this embodiment, the image processing in the image processing means 11 is
As shown in FIG. 2A, it is assumed that it is represented by a contrast adjusting unit 20 that performs a contrast adjusting process of an input image. At that time, the contrast adjusting means has a configuration as shown in FIG. 3. In FIG. 3, reference numeral 30 denotes the target pixel Pij (i,
j), the three color component values VPij (r (i, j),
From g (i, j), b (i, j)) to the surrounding pixel having the width c of Pij, the contrast adjustment amount VRPij
(Rr (i, j), Rg (i, j), Rb (i, j))
Correction information deriving means 31 for calculating the contrast adjustment amount VRPij for extracting the contrast adjustment amount VRPij obtained by the correction information deriving means 30 by limiting the effective range, and 32 for extracting the contrast adjustment amount VRPij. Is the adjusted pixel value VPdij (r in the actual pixel Pij
It is a pixel value conversion means for converting into d (i, j), gd (i, j), bd (i, j)). Further, the image synthesizing means 12
Is a selection reference value determination that determines which of the input image vi and the adjusted image v obtained by the contrast adjusting means 20 is prioritized based on the brightness in the input image 1 as shown in FIG. Based on the results of the means 100 and the selection reference value determining means 100, the input image vi and the contrast adjusting means 2
The coupling coefficient ws applied to the adjusted image v obtained with 0
(S = 1, 3; where 1 is the coupling coefficient on the input image vi, and 3 is the coupling coefficient on the adjusted image v), the coupling coefficient deriving means 101 and the coupling coefficient deriving means 101 are determined. Using the coupling coefficients w0 and w1 obtained in
The weighted average synthesizing unit 102 generates a weighted average image of the input image vi and the adjusted image v obtained by the contrast adjusting unit.

The operation of the image processing apparatus configured as described above will be described.

The color image vi is digitally input to the image processing means 11 via the image input means 10. In the case of a color image, the image input means 10 normally uses red r,
The component data of green g and blue b is image input means 10.
It is obtained with the precision of (in the case of 8 bits, a value of 0 to 255). The image input means 10 changes this value from 0.0 to 1.0.
Normalize to the value of. Next, for the digital input image, the contrast adjusting process for improving the contrast in the dark portion of the input image is performed by the contrast adjusting means 20.
Done in. The contrast adjusting means 20 will be described later with reference to FIG.
It is done like 2. In human vision, as schematically shown in FIG. 11, the pixel information (color, contrast, etc.) of Pij is not recognized only by the pixel value perceived for the target pixel Pij, but the target pixel Pij of the target pixel Pij is recognized. The pixel information of Pij is perceived by adjusting the pixel value according to the relative relationship with the information of the pixels around it. This is Edwin Lan as described in the prior art.
It is called the Retinex concept introduced by d. Even in a scene where there is uneven illumination light such that a part of the illumination is different from this perception or there is an extreme change in pixel value intensity. , It is possible to accurately recognize the color of an object. Also in the present embodiment, by using this concept, the color and detailed information in a dark portion such as a shadow are clarified.

FIG. 12 is a flowchart showing the operation of the contrast adjusting means 20 of the image processing apparatus according to this embodiment.

In FIG. 12, first, the correction information derivation means 30 causes the maximum value Vmax (rx, gx, bx) and the minimum value Vmin (r of each component of the pixel Pij in the input image.
n, gn, bn) is calculated (S1). This is because the minimum pixel value and the maximum pixel value in the output image vo are the input image vi.
In order to suppress the increase in discomfort between the two images as much as possible, the minimum pixel value and the maximum pixel value of the pixel value of the output image vo are adjusted to the minimum pixel value and the maximum pixel value of the input image vi. Processing. Next, the pixel value VPij of the target pixel Pij is regarded as the central visual field in the Land, and the area surrounding the rectangular visual field of c pixels is regarded as the peripheral visual field. Then, the correction information deriving means 30
Is a weighted average pixel value VAPi of pixel values in the peripheral visual field.
j (Ar (i, j), Ag (i, j), Ab (i,
j)) is obtained (S2), and VAPij and V
The contrast adjustment amount VRPij (Rr (i, j), Rg (i, j), Rb that leads to the relative relationship between Pij
(I, j)) is calculated (S3). As this VAPij, the pixel value VPij (r (i, j), in the peripheral visual field of the c pixel in the second term of (Equation 1) as in the prior art is used.
g (i, j), b (i, j)) and the peripheral visual field function F (x, defined by a Gaussian function as in (Equation 2) and (Equation 3).
It is also possible to define by the convolution integral value of y), and it is also possible to define the weighted average pixel value VAPij (Equation 1) independently for the three components that make up the pixel value. However, in the present embodiment, VAPij is a pixel value VPij (r (i, j), g (i) in the peripheral visual field of c pixels as shown in (Equation 4) in consideration of simplification and speedup of processing. , J), b
The average value of (i, j)) was defined. Then, the contrast adjustment amount VRPij (Rr (i, j), Rg (i,
j), Rb (i, j)) does not use the difference of logarithmic conversion values as in (Equation 1), but the ratio of the pixel value VPij for each component to the weighted average pixel value VAPij as in (Equation 5). Was defined.

[0091]

[Equation 4]

[0092]

[Equation 5]

The correction information deriving means 30 performs the contrast adjustment amount VRPij for the target pixel Pij as described above on all the images in the input image (S4). After that, the extraction means 31 obtains the average value VaR (aRr, aRg, aRb) and the standard deviation amount VdR (dRr, dRg, dRb) of each component of the contrast adjustment amount VRPij (S5), and uses the values to adjust the contrast. Quantity VRPi
Minimum value emin and maximum value emax when extracted from j
Is derived (S6). There are many methods for this derivation, but here, as a candidate for emax, aRr + α × d
Rr, aRg + α × dRg, and aRb + α × dRb are obtained, and the maximum value of these three values is emax. And
aRr-β × dRr, aRg-β as candidates for emin
XdRg and aRb-βxdRb are obtained, and the minimum value of the three values is defined as emin. By doing so, a necessary region is extracted so that the balance of each component of the extracted contrast adjustment amount VRPij is not disturbed.
Next, the pixel value conversion means 32 uses the emax and emin.
, The contrast adjustment amount VRPij (Rr (i,
j), Rg (i, j), Rb (i, j)) are converted to values in the range of 0.0 to 1.0 (S7), and the converted values are converted to Vmax (rx, gx, bx) and the minimum value Vmin (rn, gn, bn) are suppressed (S8). The contrast adjustment amount VRP thus obtained
ij is regarded as the pixel value of the contrast adjustment in the target pixel Pij, and the contrast adjustment processing ends (S
9). The pixel value conversion means 32 determines whether or not the processing is ended.

By such a series of processing, only the central portion is extracted in the distribution of the ratio of the target pixel value to the weighted average pixel value in the peripheral visual field, the variation amount from the center is emphasized, and the vicinity of the central portion is emphasized. The contrast adjustment amount VRPij of a pixel having a ratio value greatly deviating from is likely to be 1.0 or 0.0. Therefore, in a region where the difference between the target pixel Pij, which is the central visual field, and the pixel in the peripheral visual field is even small, the difference is easily emphasized, and the contrast enhancement is performed, which is buried due to the details in the shadow and the insufficient range of the input device. It becomes possible to emphasize and display the color information that has been lost. In the case of the present embodiment, the derivation of the contrast adjustment amount in each pixel is configured in a simpler form than the method based on the conventional Retinex concept. And
In the conventional method based on the Retinex concept, it has been a problem that setting of filter processing (offset, gain conversion function) when converting the contrast adjustment amount in each pixel into an actual pixel value component requires empirical knowledge. However, the advantage of the present embodiment is that it is not necessary. On the other hand, in the distribution of the ratio of the target pixel value to the weighted average pixel value in the peripheral visual field, only the central portion is extracted, and the regions before and after the central region are saturated with 0.0 or 1.0 to perform this contrast adjustment processing. In the processed image vo obtained in (1), there may be a problem that the brightness levels are generally concentrated near the center. In order to improve this problem and the decrease in the brightness level in the highlight portion having a uniform color in a very large area described in the related art, in the present embodiment, the input image vi and the image after this processing are processed. By adaptively synthesizing vo, it is possible to suppress the reduction or increase of the brightness level originally possessed by the input image vi.

In the image synthesizing process in the image synthesizing means 12, the image v after the contrast adjusting process and the input image vi are received, and the process is executed as shown in FIG.

FIG. 13 is a flowchart showing the operation of the image synthesizing means 12 of the image processing apparatus according to the first embodiment of the present invention.

Here, first, the selection reference value determining means 10
0 is the luminance y (i, i, i of each pixel Pij in the input image
j) is calculated (S11). Then, the threshold values Th_h and Th_l prepared in advance are compared with each pixel brightness y (i, j) (S12). At that time, Th_l> y (i, j)
Alternatively, if y (i, j)> Th_h, the selection reference value determination unit 100 further obtains the average luminance Ay (i, j) in the rectangular peripheral visual field region of c of Pij (S1).
3). Then, y (i, j) is | Ay (i, j) -y
When (i, j) | <Th_len (S14) is satisfied, the pixel value VPij (r (i, j), g (i, j), b (i,) in the input image vi of the target pixel Pij is satisfied.
j)) is VWP which is the value of the pixel Pij of the processed image vo
ij (WRr (i, j), WRg (i, j), WRb
(I, j)) is replaced (S15). This processing is performed in the peripheral visual field region c as described in the related art.
This is a process considering that the contrast adjustment amount in Pij tends to concentrate in the vicinity of 1.0 in the case where the variation is very small in a larger region, and this process makes it possible to uniformly highlight the input image in a large region (white ) And the black portion are excluded from the processing target, and it is an object of the present invention to suppress the large fluctuation of the brightness level which is likely to occur in the contrast adjusted image in such an area as described above.

Next, when the above conditions are not satisfied, the coupling coefficient deriving means 101 causes the luminance value y (i, j) in Pij of the input image vi and the luminance value yd at the corresponding pixel of the processed image v. (I, j) are compared (S16), and the absolute value of the error between them is len = | y (i, j) -yd (i, j
j) | is input to the prepared w3 determination function to determine the coupling coefficient w3 occupied by the processed image v (S1).
7). The coupling coefficient w1 occupied by the input image vi is w1.
= 1.0-w3. Although this w3 decision function is not uniformly decided, a threshold function g (X) such as (Equation 6) is used here for the purpose of simplicity.

[0099]

[Equation 6]

Then, the weighted average synthesizing means 102 uses w1 and w3 to calculate the weighted average pixel value VWPij (WRr (i, j) of the pixel value VPij of the input image vi in Pij and the pixel value VRPij of the processed image v. ), WRg
(I, j), WRb (i, j)) are obtained (S17) and embedded in the corresponding pixel position of the image v (S18). By performing such a series of determination and weighted average processing on all pixels (S19), the output image vo finally output by the image output means 13 is generated.

By performing the above-described configuration and processing, the image processing apparatus according to the present embodiment solves the problems of the conventional technique and makes the best use of the advantages thereof, so that the contrast of the input image can be easily and accurately obtained. Adjustments can be made.

The contrast adjustment amount VRPij (R
The definition of r (i, j), Rg (i, j), Rb (i, j)) is not uniform, and for example, as in (Equation 7), the weighted average pixel value VAPij of the pixel value VPij for each component is used. A positive constant γ preset to a value obtained by subtracting 1.0 from the ratio to
The pixel value of the input image VPij (r (i,
j), g (i, j), b (i, j)), and the contrast adjustment amount VRPij (Rr (i, j),
It is also possible to define as Rg (i, j), Rb (i, j)).

[0103]

[Equation 7]

By doing so, the contrast adjustment amount VRPij (Rr (i, j), when the pixel value variation in the peripheral visual field of c pixels is very small, which is a problem in the conventional technique,
Each component of Rg (i, j) and Rb (i, j)) is VPij
There is an advantage that it can avoid becoming close to 1.0 regardless of (r (i, j), g (i, j), b (i, j)), but it depends on the set γ. Note that there are dependencies. In addition, these processes
According to the image processing method according to the present embodiment, software processing using a central processing unit (CPU) and a digital signal processor (DSP) used in a computer or the like can be similarly realized.

(Embodiment 2) The basic configuration of an image processing apparatus according to Embodiment 2 of the present invention is the same as that of Embodiment 1, and the image processing means 11 is also shown in FIG. 2 (a). Thus, it becomes the contrast adjusting means 20. At that time, the contrast adjusting means 20 has a configuration as shown in FIG.

In FIG. 4, the correction information derivation means 30, the extraction means 31, and the pixel value conversion means 32 are the same as those in FIG. 3, so the same reference numerals are given and the description thereof is omitted. Reference numeral 40 denotes an initial setting means for setting an initial condition for deriving the correction information (calculating the contrast adjustment amount of the target pixel). The initial setting means 40 performs a process of setting the initial peripheral area size c0 to the size c of the peripheral visual field portion to be compared with the target pixel Pij which is mainly the central visual field. Further, 41 is an end determination condition for determining whether or not the contrast adjustment amount has been calculated for all of a plurality of peripheral visual field areas prepared in advance, and 42 is presently processed when the end determination condition 41 is not considered to be the end determination. It is a correction range changing means for changing the size c of the surrounding visual field to the next candidate. The image synthesizing means 12 is configured as shown in FIG. 10 as in the first embodiment.

The operation of the image processing apparatus configured as described above will be described with reference to FIGS. 10, 14 and 15. FIG. 14 is an explanatory diagram schematically showing the peripheral visual field region, and FIG. 15 is a flowchart showing the operation of the contrast adjusting means 20.

The color image vi is digitally input to the image processing means 11 via the image input means 10. In the case of a color image, the image input means 10 normally uses red r,
The component data of green g and blue b is image input means 10.
It is obtained with the precision of (in the case of 8 bits, a value of 0 to 255). The image input means 10 changes this value from 0.0 to 1.0.
Normalize to the value of. Next, for the digital input image, the contrast adjusting process for improving the contrast in the dark portion of the input image is performed by the contrast adjusting means 20.
Done in. The contrast adjusting means 20 performs as shown in FIG. The feature of the present embodiment is that contrast adjustment is performed in a plurality of peripheral vision regions prepared as shown in FIG.
This is to reduce the influence of the size of the dark part (shadow) existing in the image.

Next, the operation of the contrast adjusting means 20 will be described with reference to FIG.

In FIG. 15, first, the initial setting means 40
Is the maximum value V of each component of the pixel Pij in the input image.
max (rx, gx, bx) and minimum value Vmin (rn,
gn, bn) is calculated (S21). Next, the target pixel P
The pixel value VPij of ij is regarded as the central visual field in the Land, and the area surrounding the rectangular visual field of c pixels is regarded as the peripheral visual field. At that time, the initial setting means 40 first c =
Like c0, c0 is set as a peripheral visual field within a plurality of peripheral visual field size ck (k = 0, 1, ..., Cnum−1) prepared in advance. In this case, ck may be prepared in ascending order from the minimum size area or may be prepared in descending order from the maximum size, but it is better to arrange the changing directions of the sizes. Here, it is assumed that the images are prepared in descending order from the maximum size, and the detail is improved in the input image while the peripheral visual field area is made smaller in order. Next, in the correction information derivation means 30, for the peripheral visual field of the currently set rectangular region of c = ck, the weighted average pixel value VAPij_k (Ar_ of the pixel values in the peripheral visual field).
k (i, j), Ag_k (i, j), Ab_k (i,
j)) is calculated (S22). And all ck
Then, the end determination unit 41 determines whether or not the weighted average pixel value calculation in the peripheral visual field of the pixel Pij by the correction information derivation unit 30 is completed (S23). If it is determined that the correction information range changing unit 42 does not finish the process, the peripheral visual field area c currently set is changed to the next candidate, and the weighted average pixel by the correction information deriving unit 30 is changed again. Value calculation is performed. On the other hand, when the end determination unit 41 determines the end, the correction information derivation unit 30 determines the weighted average pixel value VAPij_k of Pij for each peripheral visual field region ck.
(Ar_k (i, j), Ag_k (i, j), Ab_k
(I, j)), the weighted average value is obtained, and the value is Pij
All weighted average pixel values VAPij (Ar (i, j), Ag
(I, j), Ab (i, j)) are set (S24).
At that time, weighting according to the size of the peripheral visual field ck may be added to the weighted average pixel value of Pij by each ck, but here, for simplification, the weighted average pixel value VAPij_k of each ck is The average pixel value is adopted as the total weighted average pixel value of Pij. Then, the correction information derivation means 30 uses the pixel value VP for each component in Pij.
The ratio of ij to the total weighted average pixel value VAPij is the contrast adjustment amount VRPij (Rr (i, j), Rg (i,
j), Rb (i, j)) (S25).

The subsequent processing is the same as in the first embodiment.
First, the minimum value emin and the maximum value emax when extracting from the contrast adjustment amount VRPij are derived (S2).
6, S27, S28). Next, this emax and emin
, The contrast adjustment amount VRPij (Rr (i,
j), Rg (i, j), Rb (i, j)) are converted to values in the range of 0.0 to 1.0 (S29), and the converted values are converted to Vmax (rx, gx, bx) and the minimum value Vmin (rn, gn, bn) are suppressed (S3
0). The contrast adjustment amount VRPij thus obtained
Is regarded as the pixel value of the contrast adjustment in the target pixel Pij (S30), and the contrast adjustment processing ends (S30a).

The image synthesizing processor also has the luminance value y (i, j) at Pij of the input image 1 as in the first embodiment.
And the brightness value yd (i, i at the corresponding pixel of the processed image 3
j), using the coupling coefficients w1 and w3, Pij
Pixel value VPij of the input image vi and the processed image v
Weighted average pixel value VWPij (WR
r (i, j), WRg (i, j), WRb (i, j))
Is calculated, and the corresponding pixel position Pij of the processed image v
Embedded in. By performing such processing on all pixels, the output image vo finally output by the image output unit 13 is generated.

By performing the above-described configuration and processing, the image processing apparatus according to the present embodiment makes use of the features of the image processing apparatus according to the first embodiment, while at the same time, the dynamic range of pixel values in the input image and the shadow The contrast of the input image can be automatically adjusted without being greatly affected by the size of the dark area, which leads to the efficiency of the contrast adjustment of the image. Note that these processes can be similarly realized by software processing using a central processing unit (CPU) used in a computer or the like and a digital signal processor (DSP) according to the image processing method according to the present embodiment. it can.

(Third Embodiment) The basic configuration of an image processing apparatus according to the third embodiment of the present invention is the same as that of the first embodiment, as shown in FIG. The image processing means 11 is also shown in FIG.
As described above, the contrast adjusting means 20 is adopted.
At that time, the contrast adjusting means 20 has a configuration as shown in FIG. FIG. 5 is a block diagram showing the contrast adjusting means 20 constituting the image processing apparatus according to the present embodiment.

In FIG. 5, 31 is the same extraction means as in FIG. 3, 50 is a signal conversion means for converting the pixel values in the input image vi into a signal (target signal) to be the target of contrast adjustment, and 51 is a signal conversion. Target correction information derivation means for obtaining the contrast adjustment amount of the target pixel Pij with respect to the target signal obtained by the means 50, and 52 obtained by the signal conversion means 50 based on the contrast adjustment amount extracted by the extraction means 31. Target signal is the actual target signal (adjusted target signal)
Target signal converting means for converting into the target signal, and 53 is the adjusted target signal obtained by the target signal converting means 52 and the signal converting means 50.
It is a signal inverse conversion means for performing an inverse conversion process of the target signal adjusted by the other signal (target signal) obtained in step 1 into the actual pixel value.

The feature of this embodiment is that the pixel value VPij (r (i, j), g in the pixel Pij of the color image is used.
(I, j), b (i, j)) are converted into other plural signals,
The contrast adjustment amount is calculated for one of the signals in the same manner as in the first embodiment, and the contrast adjustment amount of the finally adjusted target signal and the other signals held as they are at the time of conversion from the input image vi are calculated. The point is to obtain the pixel value of the pixel Pij of the processed image in FIG. 1 by performing the inverse conversion process of the conversion process performed by the signal conversion means 50. Here, in the signal conversion means 50, the pixel value VPij (r (i, j), g (i, j), b (i,
j)), for example, lightness L (i, j) and hue a * (i,
j), b * (i, j), and this lightness L (i, j)
Is the target signal for contrast adjustment. Then, by holding other hue signals as they are in the input image, in the present embodiment, the color information in the input image can be held as they are while controlling the level of brightness, so that the contrast adjusting means 20 can obtain them. With regard to the color balance of the processed image v thus obtained, the input image can be reproduced as it is, and the amount of comparison processing between the peripheral visual field and the target pixel in the target correction information deriving means 51 can be reduced.

The operation of the image processing apparatus configured as described above will be described with reference to FIG. FIG. 16 is a flowchart showing the operation of the contrast adjusting means 20 of FIG.

In FIG. 16, first, VPij (r (i, j), g (i,
j), b (i, j)) is the signal V in the La * b * space
PLij (L (i, j), a * (i, j), b * (i,
j)) by the signal converting means 50 and Pij
Among the converted value signals in, hues a * (i, j) and b *
It holds (i, j) (S31). After that, the processing from the target correction information derivation means 51 to the target signal conversion means 52 is performed with the lightness L (i, j) as the target signal.
Note that L (i, j) is normalized from 0.0 to 1.0. Therefore, it is considered that the balance of the color information of the input image vi can be accurately maintained even in the processed image v. Target correction information deriving means 51
Calculates the maximum value Lx and the minimum value Ln of the lightness L (i, j) of the pixel Pij of the input image (S32). Next, the weighted average brightness AL (i, j) of the brightness L (i, j) in the peripheral visual field of the target pixel Pij is obtained (S33), and L
The ratio between (i, j) and AL (i, j) is calculated as the brightness contrast adjustment amount RL (i, j) (S3).
4). The calculation of the brightness contrast adjustment amount RL (i, j) for the target pixel Pij as described above is performed by the target correction information deriving unit 51 for all the pixels in the input image. In the extraction means 31, the brightness contrast adjustment amount RL
The average value aRL of (i, j) and the standard deviation amount dRL are obtained (S36), and the contrast adjustment amount R of the lightness is calculated from these values.
The minimum value emin and the maximum value emax at the time of extraction from L (i, j) are determined (S37). Target signal conversion means 5
2 uses the emax and emin to convert the brightness contrast adjustment amount RL (i, j) into a value within the range of 0.0 to 1.0 (S38), and converts the converted value into the maximum value of the input image. A process for suppressing the value Lx and the minimum value Ln is performed (S3).
9). In the signal inverse conversion means 53, the brightness contrast adjustment amount RL (i, j) thus obtained is used as the target pixel Pij.
It is regarded as the contrast adjustment value of the brightness at
Using the retained hues a * (i, j) and b * (i, j), the pixel value V at the pixel Pij of the processed image v
RPij (Rr (i, j), Rg (i, j), Rb
By obtaining (i, j)) by the inverse conversion of the signal conversion used by the signal conversion means 50 (S40), the contrast adjusted image v is generated (S40a). In the signal inverse conversion means 53, the brightness contrast adjustment amount RL (i,
Since j) is converted to a value in the range of 0.0 to 1.0, the signal level is the same as the signal level when the inverse conversion is performed with the hues a * (i, j) and b * (i, j). You also need to put it back.

By taking such a series of processing, the image processing apparatus according to the present embodiment is
Compared to the case where the contrast is independently adjusted for three pixels in the color image as in 2, it is easier to maintain the color balance of the pixels in the input image, and the color in the dark portion such as a shadow in the image obtained by the adjustment is More stability can be maintained. Further, the contrast adjustment amount calculated by the relative comparison between the target pixel in the target correction information deriving unit 51 and the pixel in the peripheral visual field needs to be calculated independently for the three components in the conventional technique and the first and second embodiments. On the other hand, only one component of brightness is required, which leads to a significant reduction in processing time. Further, according to the image processing method according to the third embodiment, these processes are performed by a central processing unit (CPU) and a digital signal processor (DS) used in a computer or the like.
The same can be realized by software processing using P) or the like.

(Fourth Embodiment) The basic configuration of an image processing apparatus according to the fourth embodiment of the present invention is the same as that of the first embodiment, as shown in FIG. The image processing means 11 is also shown in FIG.
As described above, the contrast adjusting means 20 is adopted.
At that time, the contrast adjusting means 20 has a configuration as shown in FIG.

FIG. 6 is a block diagram showing the contrast adjusting means 20 constituting the image processing apparatus according to this embodiment. In FIG. 6, the signal converting means 50, the supplementary writing setting means 40, the target correction information deriving means 51, and the correction range changing means 4
2, the end determination means 41, the extraction means 31, the target signal conversion means 52, and the signal inverse conversion means 53 are the same as those in FIGS. As is apparent from FIG. 6, the contrast adjustment processing having a plurality of peripheral visual fields in the second embodiment is modified by limiting the contrast adjustment processing in the contrast adjustment means in the third embodiment to only the target signal. .

The operation of the contrast adjusting means 20 shown in FIG. 6 will be described with reference to FIG. FIG. 17 shows FIG.
7 is a flowchart showing the operation of the contrast adjusting means 20.

In FIG. 17, first, the image input means 10
VPi in the pixel Pij of the input image vi input in
j (r (i, j), g (i, j), b (i, j)) is the signal VPLi in the La * b * space in the signal converting means 50.
j (L (i, j), a * (i, j), b * (i, j))
Of the converted value signals in Pij, hue a *
(I, j) and b * (i, j) are held, and L (i, j)
Is normalized from 0.0 to 1.0 (S41). The target correction information derivation means 51 uses the lightness L of the pixel Pij of the input image.
The maximum value Lx and the minimum value Ln of (i, j) are calculated (S4
2). Next, the target correction information derivation means 51 uses this lightness L.
With (i, j) as the target signal, as in the second embodiment,
Multiple peripheral visual field regions ck (k = 0, 1, ..., Cnu
The weighted average brightness AL_k (i, j) of m-1) is obtained (S43). Then, when the end determination unit 41 determines that the calculation of the weighted average brightness for all of the predetermined peripheral visual field has been completed (S44), the target correction information derivation unit 51.
Is the weighted average value of all weighted average brightness AL
(I, j) (S45), and L (i,
The ratio j) is calculated as the brightness contrast adjustment amount RL (i, j) (S46). When the end determination unit 41 determines that all pixels have ended (S47), the extraction unit 31 obtains the average value aRL and the standard deviation amount dRL of the brightness contrast adjustment amount RL (i, j) (S48). Using the minimum value emin and the maximum value emax of the determined brightness contrast adjustment amount RL (i, j), the brightness contrast adjustment amount RL (i, j) is a value within the range of 0.0 to 1.0. Is converted to (S49, S50). The conversion value is suppressed within the maximum value Lx and the minimum value Ln of the input image,
The signal inverse conversion means 53 regards the brightness contrast adjustment amount RL (i, j) thus obtained as the brightness contrast adjustment value in the target pixel Pij (S5).
1) the retained hues a * (i, j) and b * (i,
j), the pixel values VRPij (Rr (i, j), Rg (i, j), R in the pixel Pij of the processed image 3 are used.
By obtaining b (i, j)) (S52), the contrast adjusted image v is generated (S53).

As described above, in the image processing apparatus according to the present embodiment, the contrast adjustment amount calculated by comparing the target pixel value with the average pixel value in the peripheral area in the third embodiment is used for contrast in a plurality of peripheral areas. This is an extension to the weighted average value of the adjustment amount, and there is an advantage that it is not necessary to predetermine the peripheral visual field region, which is a feature of the second embodiment, and it is easy to maintain the color balance of pixels as in the third embodiment. Therefore, it is possible to further maintain the color stability in a dark portion such as a shadow in the image obtained by the adjustment. Further, there is an advantage that the processing is simplified and the processing time can be shortened.

Further, according to the color image processing method of the fourth embodiment, these processes are also performed by software processes using a central processing unit (CPU) and a digital signal processor (DSP) used in a computer or the like. Can be realized.

(Fifth Embodiment) The basic configuration of an image processing apparatus according to the fifth embodiment of the present invention is the same as that of the first embodiment, as shown in FIG. The image processing means 11 is also shown in FIG.
As described above, the contrast adjusting means 20 is adopted, and the contrast adjusting means 20 has a configuration as shown in FIG.
The present embodiment is different from the first embodiment in that the pixel value converting means 32 in the contrast adjusting means 20 in FIG. 3 is configured as shown in FIG. 7A.

FIG. 7A is a block diagram showing the pixel value conversion means 32 according to this embodiment. As shown in FIG. 7A, the pixel value conversion means 32 is based on the average brightness calculation means 70 for calculating the average brightness in the input image vi and the average brightness obtained by the average brightness calculation means 70. , A conversion method classification means 71 for selecting a conversion method when converting the contrast adjustment amount obtained by the extraction means 31 into an actual pixel value.
And according to the conversion method obtained by the conversion method classification unit 71,
It has a pixel value estimating means 72 for converting the contrast adjustment amount obtained by the extracting means 31 into a pixel value of an actual pixel as a component. The feature of this embodiment is that the first embodiment
In addition, based on the brightness of the input image, the conversion method for converting the value obtained by the relative comparison with the pixel in the peripheral visual field into the actual pixel value is selected.

In the contrast adjusting means 20 in FIG. 3, the flow of processing other than the pixel value converting means 32 is the same as in the first embodiment, and the pixel value of the pixel Pij in the input image is in the peripheral visual field area of c pixels. First, the contrast adjustment amount is obtained by dividing by the weighted average pixel value.
Then, from this contrast adjustment amount, an effective region is extracted based on the histogram distribution of each component constituting the contrast adjustment amount, and each component is 0.0 to 1.
Converted to a value within 0. After this, step S8 in FIG.
Instead of the above, the pixel value conversion processing in the pixel value conversion means 32 is executed, and the processing is as shown in FIG.

Next, the operation of the pixel value converting means 32 of FIG. 7A will be described with reference to FIG. FIG. 18 is a flow chart showing the operation of the pixel value conversion means 32 of FIG.

In FIG. 18, first, the average brightness calculating means 70 calculates the brightness y (i, j) of the pixel Pij in the input image and obtains the brightness average ave_y (S61).
Next, the conversion method classification unit 71 selects a method in which the contrast adjustment amount is converted into an actual pixel value with the value of ave_y. In that case, many methods are conceivable, but here, the low brightness threshold Th_low and the high brightness threshold Th are used.
_High is provided, and the conversion function gg for converting the contrast adjustment amount into the actual pixel value component as in (Equation 8)
It was decided to select (x) (S62 to S66).

[0131]

[Equation 8]

In the equation (8), k_l, g_l, g_
n, k_h, and g_h are preset constants, and X represents the target component value of the contrast adjustment amount before conversion. This is to determine the part to be emphasized based on the average brightness of the input image before processing. For example,
In the case where the entire input image has low brightness, it is more impressive from the viewpoint of stereoscopic effect that the low brightness part is left as much as possible rather than the whole being rapidly brightened. Further, when the entire input image has high brightness, the degree of attention of the human being to the high brightness portion is high, and it is also necessary to hold that portion as much as possible. When a natural image has an intermediate luminance, a method of slightly darkening a luminance portion lower than the center and slightly brightening a luminance higher than the center is performed by gamma conversion of input / output values at the time of printer output or the like. The processing here is in accordance with these findings, and it is considered that a conversion method according to such knowledge is not uniformly determined by the method shown here and can be similarly used. Finally, the pixel value estimation means 72 uses the conversion function selected by the conversion method classification means 71, and the contrast adjustment amount VRPij extracted by the extraction means 31.
(Rr (i, j), Rg (i, j), Rb (i, j))
The converted value VRPoij (Rro (i, j), R
Go (i, j), Rbo (i, j)) is calculated (S6
7, S68). This is the pixel Pij in the input image.
The maximum value Vmax (rx, gx, bx) and the minimum value Vmin (rn, gn, bn) of each component of the processed image v are set as the adjusted pixel value in the pixel Pij of the processed image v, and the processed image v Is obtained (S69, S7
0). The image synthesizing unit 12 obtains a weighted average image of the image v and the input image vi, and this becomes the output image vo that is finally output.

As described above, in the image processing device according to the fifth embodiment, in the pixel value converting means 32 of the first embodiment,
Pixels to be applied by selecting a conversion method that converts the extracted contrast adjustment amount based on the average brightness in the input image after extracting an area that seems to be effective from the contrast adjustment amount obtained by comparison with surrounding pixels A value estimating means 72 is provided. By doing so, it is possible to select an appropriate pixel value conversion according to the brightness distribution in the input image, and it is possible to further enhance the contrast adjustment in the adjusted image.

In this embodiment, as the contrast adjusting means, the case of the configuration of FIG. 3 described in the first embodiment has been described. It is also possible to combine the pixel value converting means in the present embodiment with the contrast adjusting means. Further, according to the image processing method according to the fifth embodiment, these processes can be similarly realized by software processing using a central processing unit (CPU) and a digital signal processor (DSP) used in a computer or the like. it can.

(Sixth Embodiment) The basic configuration of an image processing apparatus according to the sixth embodiment of the present invention is the same as that of the first embodiment, as shown in FIG. The contrast adjusting means set as the image processing means 11 has a configuration as shown in FIG. The sixth embodiment differs from the third embodiment in that the target signal converting means 52 in the contrast adjusting means 20 in FIG.
It is configured as shown in (b).

FIG. 7B shows the target signal converting means 5 of FIG.
It is a block diagram showing 2.

As shown in FIG. 7B, the target signal converting means 52 is composed of an average target signal calculating means 73 for calculating the average value of the flat target signals in the input image vi and an average target signal calculating means 73. A conversion method classification means 71 for selecting a conversion method when converting the contrast adjustment amount of the target signal obtained by the extraction means 31 into an actual target signal value based on the obtained average value of the target signal; According to the conversion method obtained by the method classification means 71, it has a target signal estimation means 74 for converting the contrast adjustment amount of the target signal obtained by the extraction means 31 into an actual target signal value as a component. The feature of this embodiment is that, in the third embodiment, a value obtained by relative comparison with a target signal in a peripheral visual field is converted into an actual target signal value based on a target signal of an input image. The conversion method of is selected.

The flow of the contrast adjusting process in the present embodiment is processed almost in the same way as in FIG. 16 showing the third embodiment. Compared with the third embodiment, instead of step S40 of FIG. 16, the target signal converting means 74 of FIG.
The difference is that the process of is added. This FIG. 7 (b)
The processing in the target signal converting means 74 in FIG. 7 is performed by changing the processing in the pixel value converting means 32 of FIG. 7A used in the fifth embodiment to the brightness which is the target signal. Be seen. That is, the fifth embodiment shown in FIG.
In the present embodiment, the conversion function is applied only to the target signal for the pixel value conversion processing of No.
In the fifth embodiment, the average brightness of the input image is used to select the conversion method of the contrast adjustment amount extracted in step 5. However, in the present embodiment, of the lightness and hue obtained from the input image by the signal conversion means 50. The difference is that the average value of the brightness as the target signal is used. The reason why the average value of the target signal is used to select the conversion method of the contrast adjustment amount extracted by the extraction unit 31 in this way is that the calculation of the contrast adjustment amount is originally performed in the signal conversion unit 50 in the third embodiment.
The brightness R of the adjusted pixel (i, j) obtained through the target signal conversion means 52 is performed only for the brightness obtained in
Since the desired contrast-adjusted image v is obtained by inversely converting L (i, j) and the hue obtained by the signal converting means 50, from the viewpoint of simplification of processing, the brightness is the same as the brightness. There is a point that has a meaning.

Such an image processing apparatus according to the present embodiment is the same as the target signal converting means 50 of the third embodiment.
After extracting an area that seems to be effective from the contrast adjustment amount of brightness obtained by comparison with surrounding pixels, a conversion method that converts the contrast adjustment amount of the extracted target signal based on the average brightness of the input image is used. Select and apply. Therefore, the present embodiment is to improve the color stability of the pixel as in the third embodiment and to further improve the accuracy of the contrast in the fifth embodiment.

In the present embodiment, the case where the contrast adjusting means 20 has the configuration of FIG. 5 described in the third embodiment has been described, but the fourth embodiment having a plurality of peripheral visual field regions as in FIG. Contrast adjusting means 2 used in
It is also possible to combine 0 with the target signal converting means 52 in the present embodiment. In addition, these processes
According to the image processing method according to the present embodiment, software processing using a central processing unit (CPU) and a digital signal processor (DSP) used in a computer or the like can be similarly realized.

(Seventh Embodiment) The basic structure according to the seventh embodiment of the present invention is the same as that of the first embodiment as shown in FIG. Contrast adjusting means 20 which is the image processing means 11.
Has a configuration as shown in FIG. As shown in FIG. 3, the contrast adjusting means 21 includes a correction information deriving means 30, an extracting means 31,
It is composed of a pixel value conversion means 32.

FIG. 8A is a block diagram showing the image converting means 32. As shown in FIG. 8A, the image conversion unit 32 includes a reference intensity calculation unit 80 that calculates a luminance edge intensity that represents contrast information in the input image vi, and an extraction unit 3.
The conversion curve estimating means 81 for estimating the conversion curve when the contrast adjustment amount obtained in 1 is converted into the actual pixel value by the genetic algorithm, and the conversion curve of the pixel value estimated by the conversion curve estimating means 81. In addition, it has a pixel value estimating unit 72 for converting the contrast adjustment amount obtained by the extracting unit 31 into an actual pixel value as a component.

FIG. 9A is a block diagram showing the conversion curve estimating means 81 prepared for genetic algorithm estimation. As shown in FIG. 9A, the conversion curve estimation means 81
Is an initial candidate setting means 90 that prepares a chromosome set showing an initial condition and a plurality of conversion curves necessary for estimation.
And a pixel value conversion candidate calculation means 91 for obtaining an actual pixel value based on a conversion curve obtained from each chromosome, and a pixel value from each stain obtained by the pixel value conversion candidate calculation means 91 for each chromosome The evaluation value calculation means 92 for calculating the evaluation value of each conversion curve from the brightness edge strength of the image generated in step 1 and the brightness edge strength obtained from the input image, and the evaluation value calculation means 9
The fitness calculation means 93 for calculating the fitness of each chromosome from the evaluation value obtained in 2 and the selective selection, crossover and mutation recombination operation of each chromosome set obtained by the fitness calculation means 93 are performed. Recombination operation means 94 for generating the chromosome population of
And the estimation end judging means 95 for judging whether or not the optimization estimation of the conversion curve is completed and passing the optimum conversion curve to the next pixel value estimating means 72 at the time of completion.

The feature of this embodiment is that when a value obtained by relative comparison with the target signal in the peripheral visual field obtained in the first embodiment is converted into an actual pixel value, a genetic algorithm is used. The present embodiment has been devised to further sharpen the edge portion of the image obtained by the contrast adjustment amount, and the fifth embodiment has a plurality of conversion methods (conversion functions) prepared in advance for the average brightness of the input image. ) Compared with selecting more, it is possible to automatically estimate a more suitable conversion function, so there is no need to set conditions or select multiple conversion functions when classifying average brightness in advance. It has the advantage that the dependence on the image is very small.

In the flow of processing in the present embodiment, the image input means 10, the image synthesizing means 12, and the image output means 13 are the same as those in the first embodiment, and therefore will be omitted.

FIG. 19 is a flowchart showing a processing flow in the contrast adjusting means 20 which is the image processing means 11, and FIG. 20 is a flowchart showing a processing flow in the conversion curve estimating means 81 in the contrast adjusting processing. . The contrast adjustment processing according to the present embodiment will be described with reference to these drawings.

In FIG. 19, first, the correction information calculation means 30 causes the maximum value Vmax (rx, gx, bx) and the minimum value Vmin (r of each component of the pixel Pij in the input image.
n, gn, bn) is calculated (S71). Next, the pixel value VPij of the target pixel Pij is regarded as the central visual field in the Land, and the area surrounding the rectangular visual field of c pixels is regarded as the peripheral visual field. Then, the correction information calculation means 30
Weighted average pixel value VAPij of pixel values in peripheral visual field
(Ar (i, j), Ag (i, j), Ab (i, j))
Is calculated (S72), and VAPij and VPi are calculated.
The contrast adjustment amount VR that leads to the relative relationship between j
Pij (Rr (i, j), Rg (i, j), Rb (i,
j)) is calculated (S73). As this VAPij,
Similar to the first embodiment, the pixel value Vpij (r (i, j), g in the peripheral visual field of c pixels is used as in the conventional technique.
The average value of (i, j), b (i, j) is defined, and the contrast adjustment amount VRPij (Rr (i, j), Rg (i,
j) and Rb (i, j)) also define the ratio of the pixel value VPij for each component to the weighted average pixel value VAPij as in (Equation 5).

The contrast adjustment amount VRPij for the target pixel Pij as described above is calculated for all images in the input image (S74). Then, the extraction means 31
Is the average value VaR (aRr, aRg, aRb) of each component of the contrast adjustment amount VRPij and the standard deviation amount VdR.
From (dRr, dRg, dRb), the contrast adjustment amount V
Minimum value emin and maximum value e when extracted from RPij
max is derived and the contrast adjustment amount VRPij (Rr (i, j), Rg is used by using emax and emin.
Each component of (i, j) and Rb (i, j)) is converted into a value within the range of 0.0 to 1.0 (S75 to S77). The process up to this point is the same as in the first embodiment. After this, conversion curve estimation processing S78, S of the extracted contrast adjustment amount
79 is performed using a genetic algorithm as shown in FIG.

The conversion curve estimating means 81 is composed of the initial candidate setting means 90 to the estimation end judging means 95, and the processing between them corresponds to a method in which the local search ability is supplemented to the optimum solution searching method called a genetic algorithm. To do. For the genetic algorithm, for example, "Genetic
Algorithms in Search, Opt
animation and Machine Lea
"Running" (David E. Goldberg, A
dDison Wesley) and the like, and is excellent in global search capability, and even if the differential information of the evaluation function targeted by the optimal solution search problem is not obtained, if the evaluation value itself is obtained, a search is performed. It is possible to apply it to the target problem that has only the subjective evaluation of the human and the judgment standard, and its effectiveness is the method reported so far. Specifically, the processing proceeds as follows. The processing is
Each stage will be described with reference to FIGS. 21 to 23. Figure 2
1 is a schematic diagram schematically showing a chromosome structure used in a genetic algorithm in the conversion curve estimation means 81,
22 is a schematic diagram schematically showing the outline of the roulette selection method in the conversion curve estimation means 81, and FIG. 23 is a schematic diagram schematically showing the recombination operation processing of the genetic algorithm used in the conversion curve estimation means 81. is there.

{Step S81 to Step S83} First, g0 to gn-1 in FIG. 21 are used as search parameters, and the search vector Vch [A] composed of the n parameters is used as the source, and Nch elements are used. Consider the set Gv. Each element g−k [A] (k = 0, ..., N−1) of this search vector Vch [A] is called a gene because of its relation to living things, and the search vector Vch
[A] is called a chromosome. When using the genetic algorithm, first, the initial candidate setting means 90 performs a process of appropriately creating the initial set Gv of the search vector. In the initial candidate setting means 90, various methods can be considered as the method of setting the chromosome, but here, as shown in FIG.
To the contrast adjustment amount i-k before conversion existing in the range from 1.0 to 1.0, a conversion curve is set assuming that the conversion output amount o-k is converted into a value in the range from 0.0 to 1.0. . Then, it was decided to perform the chromosome notation of the search vector using a real number coding method in which n pieces of gk obtained from gk = o, ko, k-1 are arranged as they are.
For g-0, g-0 = o-0. This notation is,
The conversion curve from 0.0 to 1.0 is quantized into n pieces of data, and the value obtained by each quantization is used as the search parameter for estimating with the genetic algorithm. However, the conversion of the search parameter of the conversion curve is not uniquely determined, and other methods are possible. Also, in the chromosomal structure used here, a binary coding method in which each gk is represented in a binary number and the binary number is arranged can also be handled.

First, the initial candidate setting means 90 sets one initial search vector v by randomly selecting n real numbers from the area of -1 to 1 by a uniform random number.
The initial search vector set Gv = {Vch [A]} (A
= 0, ..., Nch-1) are set. The optimum solution search is started from this initial search vector set Gv. Then, in order to evaluate each search vector Vch [A], the luminance y (i, i,
j), and the color difference u (i, j), v (i, j) is calculated. Then, the edge strength Ec of the input image is obtained from this brightness. This Ec is defined as in (Equation 9), and the pixel Pi calculated by the Sobel operator is
It is defined as the sum of the edge strengths ec (i, j) at j.

[0152]

[Equation 9]

{Step S84} Each search vector V is obtained by using a conversion curve obtained from each element (chromosome) of the set Gv.
With ch [A], the contrast adjustment amount VRPij (Rr (i, j), Rg (i, j), R obtained by the extraction means.
b (i, j) is VRPoij [A] (Rro (i,
j, A), Rgo (i, j, A), Rbo (i, j,
Output conversion to A)). Then, the adjusted brightness value yo (i, j, A) is calculated from this value. Then, the edge strength E [A] of the adjusted image v candidate is obtained from the adjusted brightness. This E [A] is calculated by (Equation 9) like Ec. The pixel value conversion candidate means 91 performs these processes.

{Step S84} The goodness of each search vector Vch [A] obtained in the previous step as a solution is evaluated according to a preset evaluation scale, and the search vector Vch
The evaluation result of [A] is represented as an evaluation value E [A]. Here, a preset evaluation scale is referred to as an evaluation function (fit
(Ness function). This processing is performed by the evaluation value deriving means 92.
Do. Although many evaluation functions E [A] are conceivable, the edge strength of the adjusted image v candidate obtained by each search vector is used as in (Equation 9). This is because it is noted that an image that visually feels to have good contrast has not only bright and dark edges but also sharp edges.

{Step S85, Step S86} The fitness calculating means 93 calculates the fitness from the evaluation value E [A] obtained by the evaluation value deriving means 92 in order to see the suitability of each search vector. Although various functions are conceivable as a function for deriving the goodness of fit f [A], the edge strength E [A] of the adjusted image v candidate obtained by each search vector is set to the brightness yo at each pixel of the adjusted image v candidate. (I, j,
The value obtained by adding the second term relating to the distance between the maximum value yox [A] and the minimum value yon [A] in A) was divided by the edge strength Ec of the input image and used as the goodness of fit f [A]. Where φ is a constant.

[0156]

[Equation 10]

The larger the value of f [A], the better the search vector, that is, the conversion curve. The second term is
Not only is the edge strength improved, but it also has as many converted output areas as possible.

{Step S87 to Step S90} The search vector set selected in Step S86 is subjected to a genetic recombination operation such as crossover or mutation,
Create a new search vector set. The number Nch of individuals of the search vector included in the search vector set is constant here, but may increase or decrease. The shuffling operation means 94 performs this processing. Here, selective selection is performed by a roulette selection method that selects a search vector with a probability proportional to the figure conformance. The outline of the roulette selection method is as follows.

{Roulette Selection Method} (i) Search vector Vch [A] (A belonging to the set Gv
= 0, ..., Nch−1), and the sum F of the suitability of all search vectors is obtained.

(Ii) Vch [A] determines the selection probability h [k] selected as a parent for creating the next generation search vector, as shown in FIG. 22 (a). This probability is used as the search vector V
For example, the following method can be considered for assigning ch [A].

(Iii) Selection range I of each search vector
[A] is assigned to the section within [0, 1] as shown in FIG. Here, a uniform random number ra [A] within [0, 1]
RR = (ra [0], ra [1], ..., Ra
[Nch-1]) is generated. Then, as shown in FIG. 22D, a set of num [B] = I [A] satisfying (Equation 11) for ra [B] in RR Num = (num
[0], num [1], ..., Num [Nch-
1]), the Nc corresponding to this Num is obtained.
A set of h search vectors will be selected.

[0162]

[Equation 11]

By such a roulette selection method, the search vector Vch in the current search vector group Gv is
The selection of [A] is made.

Next, the new search vector group Gv thus obtained is subjected to crossover processing and mutation processing as shown in FIG. The crossover is an operation of creating a new search vector by replacing a part of the search vector represented by a finite symbol with a part of another adjustment vector as shown in FIG. Further, the mutation is an operation of changing a part of the components of the search vector selected from the search vector set to another symbol with a certain low probability as shown in FIG. 23 (b). The crossover process corresponds to a search at a position greatly apart from the current solution vector, and the mutation corresponds to a search near the current search vector. Through these two processes, the genetic algorithm creates a new set of search vectors. Although various methods have been proposed for this processing of crossover, mutation, etc., in the present embodiment, FIG.
A one-point crossover process or a two-point crossover process for exchanging two points is used. As a mutation, each gene (gk) forming a search vector is selected with a low probability from the new search vector group Gv obtained through the crossover process, and a random constant is added to perform the mutation process. Do it. At that time, we tried to maintain the diversity of the search vector by changing the probability of mutation in the half and the other half of the search vector group.

As the convergence condition of the optimization process of the search vector representing the conversion curve by these genetic algorithms, it is judged whether the number of iterations exceeds the allowable number of iterations. The processing shifts to the calculation means 91. By repeatedly executing the above-described processing steps until the convergence condition is satisfied, the search parameters forming the optimum conversion curve are estimated. In this way, many candidates are prepared globally, and symbolic recombination operations such as crossovers and mutations that are hard to be trapped in the local optimal solution (local minimum) are repeatedly performed, so that they are extracted accurately and automatically. It is possible to estimate the optimum conversion curve for converting the contrast adjustment amount obtained by the means 31.

Using the conversion curve obtained as described above, the pixel value estimating unit 72 causes the extracted contrast adjustment amount VRPij (Rr (i, j), Rg (i, j), R.
b (i, j)) is VRPoij (Rro (i, j), R
go (i, j), Rbo (i, j)), and the maximum value V of each component of the pixel Pij in the input image
max (rx, gx, bx) and minimum value Vmin (rn,
gn, bn) is suppressed. V obtained here
RPoij (Rro (i, j), Rgo (i, j), R
bo (i, j)) is generated as the pixel value in the pixel Pij of the processed image 3.

As described above, in the image processing apparatus according to this embodiment, in the pixel value converting means 32 of the first embodiment,
After extracting a region considered effective from the contrast adjustment amount obtained by comparison with the surrounding pixels, the sum of edge intensities obtained from the brightness of the input image is calculated. Next, prepare a plurality of conversion curves for converting the extracted contrast adjustment amount, calculate the total edge strength of the brightness of the final output candidate image obtained from each conversion curve, and calculate the edge of the input image of that value. A conversion curve estimating means 81 for estimating an optimum conversion curve by a genetic algorithm using a ratio to the total intensity as an evaluation value.
Is provided. Thus, when estimating the conversion function for converting the extracted contrast adjustment amount, the contrast can be further enhanced by providing the evaluation value with the degree of improvement in the edge strength related to the clarity of the details. It also leads to sharpening of the edges. Further, by using a genetic algorithm for estimating such an optimum conversion function, it is possible to automatically estimate an appropriate conversion function without depending on the input image.

In this embodiment, the contrast adjusting means 20 has been described in the case of the configuration of FIG. 3 described in the first embodiment, but in the second embodiment having a plurality of peripheral visual field regions as shown in FIG. Contrast adjusting means 20 used
It is also possible to combine the pixel value conversion means 32 in the present embodiment. Further, according to the image processing method according to the present embodiment, these processings can be similarly realized by software processing using a central processing unit (CPU) and a digital signal processor (DSP) used in a computer or the like. it can.

(Embodiment 8) The basic configuration according to the eighth embodiment of the present invention is the same as that of the first embodiment, as shown in FIG. Contrast adjusting means 20 which is the image processing means 11.
Has a configuration as shown in FIG. The present embodiment is different from the third embodiment in that the target signal converting means 52 in the contrast adjusting means in FIG. 5 is configured as shown in FIG. 8B.

FIG. 8B shows the target signal converting means 5 of FIG.
It is a block diagram showing 2.

As shown in FIG. 8B, the target signal converting means 52 calculates the reference signal strength calculating means 8 for calculating the edge strength of the target signal representing the contrast information in the input image vi.
0, target signal conversion curve estimation means 82 for estimating a target signal conversion curve when converting the contrast adjustment amount of the target signal obtained by the extraction means 31 into an actual target signal value, and target signal conversion Based on the conversion curve of the target signal estimated by the curve estimation means 82, the target signal estimation means 74 for converting the contrast adjustment amount of the target signal obtained by the extraction means 31 into an actual target signal value is used as a component. To have. Then, the target signal conversion curve estimating means 82 is configured to execute the genetic algorithm as in the case of the seventh embodiment, and its configuration diagram is shown in FIG. 9 (b). The target signal conversion curve estimating means 82 is the initial candidate setting means 90.
A target signal conversion candidate calculation means 96 for obtaining an actual target signal obtained from each conversion curve candidate, and edge strength of each candidate obtained from the target signal obtained by the target signal conversion candidate calculation means 96 and the input image An evaluation value calculation means 92 for calculating an evaluation value of each conversion curve from the edge strength obtained from the target signal, and a fitness degree calculation means 93 for calculating a fitness degree from each evaluation value obtained by the evaluation value calculation means 92. , The recombination operation means 94 for performing selective selection, crossover or mutation recombination operation of each candidate set obtained by the goodness-of-fit calculation means 93, and determination as to whether or not the optimization estimation of the conversion curve is completed, At that time, it has an estimation end determination means 95 that passes an optimum target signal conversion curve to the next target signal estimation means 74 as a constituent element.

The feature of this embodiment is that the value obtained by the relative comparison with the target signal in the peripheral visual field obtained in the third embodiment is converted into the actual target signal value (brightness value). The image is obtained by using a genetic algorithm to further sharpen the edge portion of the image obtained by the contrast adjustment amount of the lightness value, and is prepared in advance with the average lightness as in the sixth embodiment. It is considered to be more versatile than selecting a conversion method (conversion function).

The flow of processing in this embodiment is the same as that in the third embodiment for the image input means 10, the image synthesizing means 12, and the image output means 13. Image processing means 11
5 and FIG. 8B, the processing is modified from the processing of the seventh embodiment shown in FIG. 19 so that only the target signal is subjected to the contrast adjustment. Will be things. The processing in the target signal conversion curve estimating means 82 by the genetic algorithm used at that time is also the same as in the calculation of the evaluation value indicating the superiority or inferiority of each target signal conversion curve candidate in the seventh embodiment shown in FIG. The signal obtained by modifying the edge strength of the target signal obtained in step 1 is used.

The image input means 10, the image synthesizing means 12, and the image output means 13 perform the same operations as in the first embodiment. In the contrast adjustment processing, first, the input image vi is La * b.
* The lightness is normalized from 0.0 to 1.0 while being converted into a signal in space and preserving the hue. The target correction information deriving unit 51 calculates the maximum value Lx and the minimum value Ln of the brightness of the input image, and calculates the weighted average brightness AL (i, j) of the brightness L (i, j) in the peripheral visual field of the target pixel Pij, Then, the ratio between L (i, j) and AL (i, j) is calculated as the brightness contrast adjustment amount RL (i, j). The extraction means 31 determines the contrast adjustment amount RL of the brightness.
The minimum value emin and the maximum value e when the effective area is extracted from the contrast adjustment amount RL (i, j) of the brightness determined by the average value and the standard deviation amount of (i, j)
Using max, the brightness contrast adjustment amount RL (i,
Convert j) to a value in the range 0.0 to 1.0. After that, the processing shifts to the target signal converting means 52 shown in FIG. First, the reference intensity calculation means 80 calculates the edge intensity of the brightness representing the contrast information in the input image 1,
The genetic algorithm estimates a target signal conversion curve when converting the brightness contrast adjustment amount obtained by the extraction means 31 into an actual brightness value. The target signal conversion curve estimation means for performing this estimation is configured as shown in FIG. 9B, and sets the conversion curve candidate, calculates the target signal obtained by each conversion curve candidate, and evaluates the calculated conversion curve candidate. Is calculated, the fitness of each candidate is calculated, the candidate group is rearranged, and the estimation end determination is performed in order to estimate the optimum target signal conversion curve. Then, using the obtained conversion curve, the contrast adjustment amount of the lightness obtained by the extraction means 31 is converted into an actual lightness value, and the lightness maximum value Lx and minimum value Lx of the input image are converted.
Within n, the contrast adjusted image v is generated by calculating the actual pixel value by inverse conversion from the hue of each pixel of the input image and the adjusted brightness. The processing is performed in this way.

As described above, the image processing apparatus according to the present embodiment has the effect of enhancing the color stability of pixels in dark areas such as the shadows of the third and fourth embodiments, and the contrast of the seventh embodiment. And has the effect of promoting sharpening of the edge portion. In the present embodiment, the case of the configuration of FIG. 5 described in the third embodiment as the contrast adjusting means has been described, but the contrast used in the fourth embodiment having a plurality of peripheral visual field regions as shown in FIG. It is also possible to combine the target signal converting means 52 in the present embodiment with the adjusting means. Further, according to the image processing method according to the eighth embodiment, these processes can be similarly realized by software processing using a central processing unit (CPU) and a digital signal processor (DSP) used in a computer or the like. You can

(Ninth Embodiment) The basic configuration according to the ninth embodiment of the present invention is the same as that of the first embodiment, as shown in FIG. Unlike the first to eighth embodiments, the image processing means 11
Is composed of the color adjusting means 21.

FIG. 24 is a block diagram showing the color adjusting means 21 which constitutes the image processing means.

As shown in FIG. 24, the color adjusting means 21 divides the input image into a plurality of small areas, and an image dividing means 24.
0 and a representative color extracting means 241 for obtaining a representative color representative of the color data in each small area obtained by the image dividing means 240.
And a color conversion table 243 in which color data after color conversion prepared in advance is collected, and a representative color conversion color selection unit that selects a color closest to the representative color obtained by the representative color extraction unit 241 from the color conversion table 243. 242 and the representative color obtained by the representative color conversion color selection unit 242 and the distance between the conversion colors, the selected conversion colors are finely adjusted, and the representative color is changed to the converted color. It has a color conversion means 244 for conversion as a component.

FIG. 26 is a block diagram showing the representative color extracting means 241.

As shown in FIG. 26, the representative color extracting means 241.
Is an initialization unit 260, a division axis determination unit 261, a cluster division unit 262, and a cluster representative determination unit 263.
It has a convergence determination unit 264, a cluster division end determination unit 265, and a representative color output unit 266 as constituent elements.

The operation of the image processing apparatus thus configured will be described with reference to FIGS. 25 and 27. Figure 2
5 is a flowchart showing the color adjustment processing in the color adjusting means 21, and FIG. 27 is a flowchart showing the processing in the representative color extracting means 241.

The color image vi is digitally input to the image processing means 11 via the image input means 10, and each component value of the pixel value is normalized to a value of 0.0 to 1.0. Next, the color adjusting means 21 starts the color adjusting process on the digital input image. As shown in FIG. 25, first, the input image is composed of a plurality of small areas CGs (s = 0, ...
..., NCnum-1) (which may be other than the rectangular area) is divided by the image dividing means 240 (S91). here,
NCnum represents the number of small areas.

After that, the representative color extracting unit 241 obtains m representative colors representing each area for each small area (S101, FIG. 27). At this time, m is fixed and described, but it is also possible to change the number of representative colors extracted from the target area based on the color distribution in the target area. It is possible to further reduce the color balance and rattling at the edge boundary.

The processing of the representative color extracting means 241 is as shown in FIG. As the processing in the representative color extracting means 241,
Pixel value VPij (r (i, j), g in pixel Pij
The clustering method was applied to the space of (i, j), b (i, j). In particular, this time, a vector quantization (VQ) method that divides a vector space having a plurality of elements into a plurality of clusters based on the distribution status of the sample group is used to connect adjacent quantization representative vectors. Vertical of line segment 2
A k-mean in which a cluster area frame is set as an equal line
It was constructed based on the method. A clustering method other than this can be used. First, the initialization means 2
60, it is assumed that all the color data VPij in the entire target area belong to one cluster, and one representative color vector VC1 = (Cr1, Cg1, Cb1) is set to the center of gravity vector of all the color data in CGs. Yes (S102). Here, nnn represents the number of color data in the entire target color area, and C
r1 represents the r component of the representative color vector VC1, Cg1 represents the g component, and Cb1 represents the b component. Further, thereafter, the pixel value VPij (r (i,
j), g (i, j), b (i, j)) is a color vector VPk (rk, gk, b) using the serial number k in the area.
k).

Next, the division axis determining means 261 determines the representative color vector VCj representing the cluster j in the current nn clusters and the nn-m color vectors uVPm = (urm, ugm, ubm)
Absolute value (| urm-Crj |, | ugm-Cgj of the difference of each element which comprises (m = 0, ..., nn-m-1)
|, | Ubm-Cbj |) is calculated, and the sum (tr =
Σ | urm-Crj |, tg = Σ | ugm-Cgj |,
tb = Σ | ubm-Cbj |) is calculated for each element. Then, it is considered that the current cluster can be divided in the direction of the axis having the largest value among tr, tg, and tb (S103). The cluster dividing unit 262 has the current representative color vector VCj as the center, and the dividing axis determining unit 261.
Two temporary representative color vectors dVCj obtained in the axial direction
And dVCj + 1 are set (S104). For example, when the r-axis is selected by the division axis determining means 261, a certain small positive constant gamma is used to dVCj = (Crj-
gamma × tr, Cgj, Cbj) and dVCj + 1 =
(Crj + gamma × tr, Cgj, Cbj) is set by the cluster dividing means 262. Two temporary representative color vectors dVCj and dVC from one representative color vector VCj
Since j + 1 is generated and the processing in the division axis determining unit 261 and the cluster dividing unit 262 is performed for all nn clusters at the present time, the number of generated clusters is 2nn. Note that this method is not unique and other methods are possible. Next, the cluster representative determining means 26
In the third cluster representative determining means, the cluster dividing means 262
2nn provisional representative color vectors dVCi newly obtained in
Euclidean distance disk between input and input color vector VPk
[K, i] (i = 1, ..., 2nn) is calculated, and i = i_min at which disk [k, i] is minimized for i.
Processing is performed so that VPk belongs to (S10).
5). Then, after each input color vector VPk can be assigned to the cluster, the center of gravity of the input color vector in the cluster i is set as the representative color vector VCi of the cluster i (S1).
06). The convergence determining unit 264 determines the cluster based on the Euclidean distance between the representative color vector VCi of the cluster i obtained by the cluster representative determining unit 263 and the temporary representative color vector dVC obtained by the cluster dividing unit 262. It is determined whether the representative color vector VCi has converged (S107). If it has not converged, the process moves to the cluster dividing unit 262, and the current representative color vector VCj
The provisional representative color vectors dVCj and dVCj + 1 are reset to two in the axial direction obtained at 261 with respect to. Specifically, dVCj = (Crj-2 × gamma ×
tr, Cgj, Cbj) and dVCj + 1 = (Crj + 2
Xgamma × tr, Cgj, Cbj) is set by the cluster dividing means 262, and the input color vector VPk is distributed to the 2nn clusters by using the 2nn temporary representative color vectors dVCi obtained again by the cluster representative determining means 263. Return to processing. When the convergence determination unit 264 determines that the number of clusters has converged, the cluster division end determination unit 265 determines whether or not the number of clusters satisfies a predetermined number m of cluster divisions (S108). The representative of the cluster i finally obtained by the representative color output unit 266 is output as the representative color VCi (Cri, Cgi, Cbi) (S109, S92).

As described above, the representative color extracting means 241 and the processing therefor perform the processing of using VQ to divide the color data in the target color space into a plurality of clusters based on the distribution thereof. However, this method is not unique. Instead of VQ, it is also possible to simply divide the maximum value and the minimum value in each color so that the histograms in each cluster are the same. VQ with features that can be accurately divided into clusters according to the statistical distribution of
The method was used. Also, as a clustering other than this, a method represented by a self-organizing neural network (for example, Kohonen's self-organizing network) can be used. Further, here, as the representative color vector representing each cluster, the division axis determining means 26 is used.
1 and the cluster representative determining means 263 set the centroid vector of the color vector VPk belonging to each cluster, but it is also possible to divide the cluster by selecting the optimal color vector belonging to each cluster. When the cluster representative determining unit 263 allocates the color vector VPk to each cluster, the Euclidean distance disk between the current representative color vector VCi and the color vector VPk.
It is assumed that VPk belongs to the cluster i having the smallest [k, i]. However, in addition to the Euclidean distance, the representative color vector V
It is also possible to use the sum of absolute differences between Ci and each element of the color vector VPk.

The area CGs (s = 0, ...
..., NCnum-1) representative color VCj [S] (Cr
j [S], Cgj [S], Cbj [S]) (j = 0, ...
.., m-1) is the color Vtu (rtu, gtu, btu) (u = 0, ...) In the color conversion table of the color conversion means 244.
., Tnum-1) is compared (S93). Where T
num indicates the number of color data in the color conversion table. Then, the Euclidean distance len [s, with the representative color VCj [S] is
u, j] is the minimum color data in the table u = u_b
Find [j, s] and use this as temporary conversion target color data. Furthermore, the len [s, u_b, j] len [s, u # b,
j] based on the color Vtu_b (rtu_b, gtu_b, b
Modify tu_b). As an example of the correction method,
Considering that the color does not change abruptly, the provisional target color is modified as in (Equation 12) and the conversion target color VT [s, j] (rt [s, j], for the representative color VCj [S], gt [s,
j], bt [s, j]) (S94). Here, δ is a small positive constant.

[0188]

[Equation 12]

In the color conversion means 244, the target area CGs
Representative color VCj of (s = 0, ..., NCnum-1)
[S] (j = 0, ..., M-1) is converted to the target color VT
[S, j] (rt [s, j], gt [s, j], bt
The processed image v is generated by changing to [s, j]).

The image synthesizing means 12 generates a weighted average image of the processed image v obtained by the color adjusting means 21 and the input image vi, and this is the image v output from the image output means 13.
It becomes o. Since the processing in the image synthesizing means 12 is the same as that in the contrast adjustment of the first embodiment, it is omitted here.

By constructing the above-mentioned configuration, the image processing apparatus according to the present embodiment has an advantage that it is not necessary to define both the color before conversion and the color after conversion in advance. In addition, by extracting the representative color with high accuracy and generating a weighted average composite image with the original input image, there is concern about the color balance after conversion that occurs in the case of the method of simply using the color histogram for color adjustment. It is also possible to suppress qualitativeness to some extent. Note that these processes are performed according to the image processing method according to the present embodiment, such as a central processing unit (CPU) and a digital signal processor (DS) used in a computer or the like.
The same can be realized by software processing using P) or the like.

[0192]

As described above, according to the image processing apparatus of the first aspect of the present invention, the image input means for converting the input analog image signal into digital image data,
Image processing means for performing desired image processing on the converted digital image data to generate processed image data, and image synthesizing means for synthesizing the processed image data and digital image data to generate synthetic image data, Image processing means for converting the composite image data into an analog composite image signal, and the image processing means comprises a contrast adjusting means for performing contrast adjustment on the digital image data or a color for performing color adjustment on the digital image data. Since it is an adjusting means, it is possible to perform contrast adjustment or color adjustment with respect to the input image without requiring any experience. Therefore, it is possible to easily adjust the contrast of the captured image using only the image having the captured bright and dark portions. You can
Further, there is an advantageous effect that it is possible to eliminate the dependence on the input image and the loss of color balance after conversion.

According to the image processing device of the second aspect,
The image processing apparatus according to claim 1, wherein the contrast adjusting means extracts the correction information deriving means for obtaining the contrast adjusting amount of the target pixel and the effective range from the obtained contrast adjusting amount of the target pixel. Means and
The contrast adjustment amount of the target pixel is obtained by having a pixel value conversion unit that converts the pixel value of the target pixel into an adjustment pixel value in a limited range based on the contrast adjustment amount of the target pixel, and the contrast adjustment amount of the target pixel. Since the pixel value of the target pixel is converted into the adjustment pixel value in a limited range based on the above, it is possible to automatically and surely perform the contrast adjustment or the color adjustment on the input image without experience. The advantageous effect that it can be obtained.

According to the image processing apparatus of claim 3,
The image processing apparatus according to claim 1, wherein the contrast adjustment unit sets an initial condition for setting a contrast adjustment amount of the target pixel and a pixel comparison range, and a target based on the pixel comparison range. A correction information derivation unit that obtains the contrast adjustment amount of the pixel, an end determination unit that determines whether or not the contrast adjustment processing based on the obtained contrast adjustment amount of the target pixel has ended in all pixel comparison ranges, and an end determination unit. If the end determination is not made, the correction range changing means for changing the pixel comparison range and passing the processing to the correction information deriving means, and the contrast obtained from the plurality of pixel comparison ranges if the end determination means makes the end determination Extraction means for limiting the effective range from the adjustment amount and extracting the target image within the limited range based on the contrast adjustment amount of the target pixel. And a pixel value conversion means for converting the pixel value of the pixel value into an adjusted pixel value, the contrast adjustment amount calculated by comparing the target pixel value and the average pixel value in the peripheral region with the contrast adjustment amount in the plurality of peripheral regions. Since it can be expanded to the weighted average value of, the effect of the input image and the effect of setting a constant indicating the size of the peripheral visual field can be reduced.

According to the image processing device of the fourth aspect,
The image processing apparatus according to claim 1, wherein the contrast adjusting unit converts the pixel value in the input image into a target signal to be a target for contrast adjustment, and a target signal obtained by the signal converting unit. On the other hand, target correction information deriving means for obtaining the contrast adjustment amount of the target pixel, extraction means for limiting and extracting the effective range from the contrast adjustment amount of the target pixel obtained by the target correction information deriving means, and the contrast of the target pixel In the limited range based on the adjustment amount, the target signal conversion unit that converts the target signal obtained by the signal conversion unit into the adjustment target signal to generate the adjusted target signal, and the adjusted target signal and the signal conversion unit. The input image is brightened by including a signal inverse conversion unit that performs inverse conversion processing of the pixel value in the input image into the adjusted pixel value based on the obtained target signal. , The value is converted into information such as hue once, and this brightness is used as the target signal, and the brightness contrast adjustment amount is calculated by comparing the brightness of the target pixel with the average brightness in the surrounding area. Since it is possible to extract, the pixel value of the image after adjustment is obtained from the extracted brightness and the hue obtained from the input image, and the obtained contrast adjusted image and the input image are combined with an appropriate coupling coefficient. An advantageous effect that the contrast of the input image can be adjusted is obtained by performing the weighted average composition.

According to the image processing apparatus of the fifth aspect,
The image processing apparatus according to claim 1, wherein the contrast adjusting unit converts the pixel value in the input image into a target signal to be a target for contrast adjustment, and a contrast adjustment amount for the converted target signal. Initial setting means for setting an initial condition and a pixel comparison range, a target correction information deriving means for obtaining a contrast adjustment amount of each pixel with respect to a target signal converted based on the pixel comparison range, and a calculated for each pixel. An end determination unit that determines whether or not the contrast adjustment processing based on the contrast adjustment amount has ended in all pixel comparison ranges, and if the end determination unit does not determine the end, the pixel comparison range is changed and the target correction information is changed. Effective than the contrast adjustment amount of each pixel when the end range is determined by the correction range changing means that passes the processing to the deriving means And a target signal conversion unit that converts the target signal converted in the limited range based on the contrast adjustment amount of each pixel into an adjustment target signal to generate an adjusted target signal. , The target pixel value and its peripheral region by having the signal inverse conversion means for performing the inverse conversion processing of the pixel value in the input image to the adjusted pixel value by the adjusted target signal and the target signal converted by the signal conversion means. Since it is possible to extend the contrast adjustment amount calculated by comparison with the average pixel value in the above to the weighted average value of the contrast adjustment amounts in a plurality of peripheral regions, there is an advantageous effect that highly accurate contrast adjustment can be performed. can get.

According to the image processing apparatus of the sixth aspect,
In the image processing device according to claim 2 or 3, the pixel value conversion means calculates an average brightness in the input image and an average brightness calculation means, and based on the calculated average brightness, obtains the obtained contrast adjustment amount. According to the conversion method obtained by the conversion method classification means and the conversion method classification means for selecting the conversion method when converting the pixel value in the input image to the adjusted pixel value based on
By having a pixel value estimating means for converting the obtained contrast adjustment amount into a pixel value of an actual pixel, after extracting an area that seems to be effective from the contrast adjustment amount obtained by comparison with surrounding pixels, Since the conversion method for converting the extracted contrast adjustment amount can be selected and applied based on the average brightness in the input image, the advantageous effect that the contrast adjustment can be performed easily and reliably is obtained. .

According to the image processing device of the seventh aspect,
The image processing apparatus according to claim 4 or 5, wherein the target signal converting means calculates an average value of the target signals obtained by the signal converting means in the input image, and generates an average target signal. A conversion method classifying unit for selecting a conversion method when converting the obtained target signal into the adjustment target signal based on the generated target adjustment signal, and the conversion method. According to the conversion method obtained by the classification means, by having a target signal estimation means for converting the obtained contrast adjustment amount of the target signal into the target signal value of the actual pixel, the target obtained by comparison with surrounding pixels After extracting an area that seems to be effective from the contrast adjustment amount of the signal (for example, brightness), the extracted target signal is extracted based on the average target signal value (average brightness) in the input image. It is possible to selectively apply conversion method for converting a last adjustment amount, advantageous effect can be performed easily and reliably contrast adjustment can be obtained.

According to the image processing apparatus of the eighth aspect,
In the image processing device according to claim 2 or 3, the pixel value conversion means obtains based on the reference intensity calculation means for calculating a reference intensity value indicating the contrast intensity in the input image and the calculated reference intensity value. The obtained contrast adjustment amount is actually calculated by using the conversion curve estimating means for estimating the conversion curve when converting the pixel value in the input image into the adjusted pixel value based on the obtained contrast adjustment amount and the estimated conversion curve. Edge value obtained from the brightness of the input image after extracting an area that seems to be effective from the contrast adjustment amount obtained by comparison with surrounding pixels Since the sum of intensities can be calculated, multiple conversion curve candidates for converting the extracted contrast adjustment amount are prepared, and the final output candidate image obtained from each conversion curve. Calculate the edge intensity sum of the brightness, advantageous effect that the optimum conversion curve the ratio edge intensity sum of the input image of that value as an evaluation value can be estimated by the genetic algorithm is obtained.

According to the image processing device of the ninth aspect,
The image processing apparatus according to claim 4 or 5, wherein the target signal conversion unit is a reference intensity calculation unit that calculates a reference intensity value indicating a contrast intensity in the input image with respect to the target signal obtained by the signal conversion unit. A target signal conversion curve estimating means for estimating a conversion curve when converting the target signal obtained based on the contrast adjustment amount of the obtained target signal based on the calculated reference intensity value, By using a target signal estimation unit that converts the obtained target signal into an adjustment target signal using the estimated conversion curve, it is effective from the target signal (brightness) contrast adjustment amount obtained by comparison with surrounding pixels. After extracting the likely region, the sum of the edge strength of the input image can be calculated from the target signal of the input image, so the contrast adjustment amount of the extracted target signal is converted. The conversion curve when a plurality candidate prepared,
It is possible to estimate the total edge strength of the target signal of the final output candidate image obtained from each conversion curve, and to estimate the optimum conversion curve with a genetic algorithm using the ratio of that value to the total edge strength of the input image as an evaluation value. The advantageous effect that it can be obtained.

According to the image processing apparatus of the tenth aspect, in the image processing apparatus of the eighth aspect, the conversion curve estimating means includes an adjustment vector of an adjustment vector composed of a preset fixed number of adjustment parameters. Pixel value conversion for converting the pixel value in the input image into the adjusted pixel value based on the obtained contrast adjustment amount using the initial candidate setting means for setting the initial candidate group and each vector in the adjustment vector group at the present time Candidate calculation means, evaluation value calculation means for evaluating the contrast strength by each conversion curve candidate using the converted pixel value obtained from the reference intensity value and each vector in the adjustment vector group at the present time, and the previous evaluation value The fitness calculation means for calculating the fitness of each candidate obtained by the derivation means, and the recombination operation of the current candidate based on the calculated fitness of each candidate Contrast adjustment amount obtained by comparison with surrounding pixels by having a recombination operation means for generating a new adjustment vector set and an estimated end determination means for determining whether or not the optimization of the adjustment vector is finished. Since it is possible to calculate the sum of the edge strengths obtained from the brightness of the input image after extracting the more effective area, prepare a plurality of conversion curves for converting the extracted contrast adjustment amount, It is said that it is possible to estimate the optimum conversion curve with a genetic algorithm by calculating the edge strength sum of brightness of the final output candidate image obtained from the conversion curve and using the ratio of that value to the total edge strength of the input image as an evaluation value. An advantageous effect is acquired.

According to the image processing device of the eleventh aspect, in the image processing device of the ninth aspect, the target signal conversion curve estimating means is an adjustment constituted by a preset fixed number of adjustment parameters. Using the initial candidate setting means for setting an initial candidate group of vectors and each vector in the adjustment vector group at the present time, the obtained target signal is set as the adjustment target signal based on the contrast adjustment amount of the obtained target signal. Using the target signal conversion candidate calculation means for conversion and the reference intensity value and the converted target signal value obtained from each vector in the adjustment vector group at the present time, the contrast intensity of the target signal by each conversion curve candidate is evaluated. Evaluation value calculating means, a fitness calculating means for calculating the fitness of each candidate obtained by the evaluation value deriving means, and By having a recombination operation means for generating a new adjustment vector set by performing a recombination operation on the candidate, and an estimated end determination means for determining whether or not the optimization of the adjustment vector is completed, The target signal (brightness) obtained by the comparison of the contrast adjustment amount is extracted, and then the sum of the edge strength of the input image can be calculated from the target signal of the input image. Prepare multiple conversion curves when converting the contrast adjustment amount of the signal,
It is possible to estimate the total edge strength of the target signal of the final output candidate image obtained from each conversion curve, and to estimate the optimum conversion curve with a genetic algorithm using the ratio of that value to the total edge strength of the input image as an evaluation value. The advantageous effect that it can be obtained.

According to the image processing apparatus of the twelfth aspect, in the image processing apparatus of the first aspect, the color adjusting means divides the input image into a plurality of small areas, and the image dividing means. Representative color extraction means for obtaining a representative color representative of the color data in each small area obtained in step 1, a color conversion table prepared in advance for the color data after color conversion, and a representative color obtained by the representative color extraction means. Representative color conversion color selecting means for selecting a color closest to the color as a conversion color from the color conversion table,
Color conversion for finely adjusting each selected conversion color based on the distance between the representative color and the conversion color obtained by the representative color conversion color selection means, and converting the representative color into the converted color after the fine adjustment. By including the means, it is possible to divide the input image into a plurality of detailed areas and select the representative color based on the statistical distribution of the colors in each area. The colors are compared to extract the converted color closest to each representative color, and the selected color data after conversion is finely adjusted based on the distance between the two colors to determine the representative color in each area. It is possible to convert to this finely adjusted conversion color, and to perform color adjustment of the input image by performing weighted average synthesis of the image after color adjustment thus obtained and the input image based on an appropriate coupling coefficient. The advantageous effect that it is possible is obtained.

According to the image processing apparatus of the thirteenth aspect, in the image processing apparatus of the twelfth aspect, the representative color extracting means sets the color data in the divided small areas to
Set the group in the start state when performing sequential division processing, classify all color data into each group, and based on the initializing means for obtaining the representative color of each group and the distribution of the color data belonging to the division target group. In addition, according to the division axis determining means for determining the component of interest when dividing the target group, and dividing the target group into a plurality according to the obtained component of interest, the color data belonging to the target group is distributed to the group obtained after the division. A cluster dividing unit, a cluster representative color determining unit that obtains a representative color of the color data that belongs to each of the obtained groups, and whether or not the representative colors have converged. If they have not converged, the current representative color If it is determined by the convergence determination means that the processing moves to the cluster division means to perform the group division again based on It is determined whether or not a predetermined number of representative colors from the target area obtained in step 1 are obtained, and if not obtained, cluster division end determination means to which processing to the division axis determination means shifts, and cluster division end When it is determined by the determination means that the input image is divided into a plurality of detail areas by having a representative color output means for outputting a predetermined number of obtained representative colors, the color in each area is divided. Since the representative color can be selected by the statistical distribution of, the color in the color conversion table prepared in advance is compared with the representative color, and the converted color closest to each representative color is extracted. The color data after conversion selected based on the distance between the colors can be finely adjusted, and the representative color in each area can be converted into this finely adjusted conversion color. And the input image based on the appropriate coupling coefficient Advantageous effect that it is possible to perform color adjustment of the input image by weighted average synthesis is obtained.

According to the image processing apparatus of the fourteenth aspect, in the image processing apparatus of any one of the first to thirteenth aspects, the image synthesizing means is the input image and the adjusted image obtained by the image processing means. Selection criterion value determining means for deciding which of the two images is to be prioritized, and the coupling coefficient applied to the input image and the adjusted image obtained by the image processing means based on the determination result of the selection reference value determining means. A weighted average synthesizing means for generating a weighted average image of the input image and the adjusted image obtained by the image processing means by using the determined coupling coefficient deriving means and the coupling coefficient of each image determined by the coupling coefficient deriving means. By having the above, it is possible to obtain the contrast adjustment amount by comparing the pixel value of the target pixel and the average pixel value in the peripheral region, and extract the contrast adjustment amount that is considered to be effective. , Advantageous effect that it is possible to perform contrast adjustment of the input image by such a the resulting contrast adjustment image and the input image to the weighted-averaging-synthesis based on appropriate coupling coefficient is obtained.

According to the image processing method of the fifteenth aspect, the image input step of converting the input analog image signal into digital image data, and the processed image data by performing desired image processing on the converted digital image data. An image processing step for generating a composite image data by combining processed image data and digital image data, and an image output step for converting the composite image data into an analog composite image signal. Since the image processing step is a contrast adjustment step for performing contrast adjustment on the digital image data or a color adjustment step for performing color adjustment on the digital image data, the image processing step can be performed on the input image without experience. You can adjust the contrast or color with It is possible to easily adjust the contrast of the captured image by using only the image having the bright and dark portions that are formed, and to obtain the advantageous effect that the dependence on the input image and the loss of the color balance after conversion can be eliminated. .

According to the image processing method of the sixteenth aspect, in the image processing method of the fifteenth aspect, the contrast adjustment step includes a correction information derivation step of obtaining a contrast adjustment amount of the target pixel, and the obtained target pixel. And a pixel value conversion step of converting the pixel value of the target pixel into an adjusted pixel value in the limited range based on the contrast adjustment amount of the target pixel. By having the contrast adjustment amount of the target pixel and converting the pixel value of the target pixel into the adjustment pixel value in a limited range based on the contrast adjustment amount of the target pixel, it is possible to input without any experience. The advantageous effect that the contrast adjustment can be automatically and reliably performed on the image is obtained.

According to the image processing method of the seventeenth aspect, in the image processing method of the fifteenth aspect, the contrast adjustment step includes an initial condition and a pixel comparison range for calculating the contrast adjustment amount of the target pixel. The initial setting step for setting, the correction information deriving step for obtaining the contrast adjustment amount of the target pixel based on the pixel comparison range, and the contrast adjustment processing based on the obtained contrast adjustment amount of the target pixel are completed in all pixel comparison ranges. An end determination step for determining whether or not it has been performed, a correction range changing step for changing the pixel comparison range and passing the process to the correction information derivation step if the end determination step has not been completed, and an end determination step If it is determined, the effective range is limited from the contrast adjustment amount obtained from multiple pixel comparison ranges. By including an extraction step of extracting and a pixel value conversion step of converting the pixel value of the target pixel into an adjusted pixel value in a limited range based on the contrast adjustment amount of the target pixel, the target pixel value and the average in the surrounding area Since the contrast adjustment amount calculated by comparison with the pixel value can be expanded to the weighted average value of the contrast adjustment amounts in a plurality of peripheral regions, the influence of the input image and the setting of a constant indicating the size of the peripheral visual field region can be set. The advantageous effect that the influence by can be reduced can be obtained.

According to the image processing method of the eighteenth aspect, in the image processing method of the fifteenth aspect, the contrast adjusting step converts the pixel value in the input image into a target signal to be a target for the contrast adjustment. Effective from the signal conversion step, the target correction information derivation step for obtaining the contrast adjustment amount of the target pixel with respect to the target signal obtained in the signal conversion step, and the contrast adjustment amount of the target pixel obtained in the target correction information derivation step. And an extraction step of limiting and extracting a range and a target signal obtained in the signal conversion step in a limited range based on the contrast adjustment amount of the target pixel is converted into an adjustment target signal to generate an adjusted target signal. The input signal is converted by the target signal conversion step, the adjusted target signal and the target signal obtained in the signal conversion step. By having the signal inverse transform step of performing inverse transform processing the pixel values of the inner to the adjusted pixel values,
The input image is converted once into information such as brightness and hue, and this brightness is used as the target signal, and the brightness contrast adjustment amount is calculated by comparing the brightness of the target pixel with the average brightness in the surrounding area. Since the amount of contrast adjustment can be extracted, the pixel value of the adjusted image is obtained from the extracted lightness and the hue obtained from the input image, and the obtained contrast adjusted image and the input image are appropriately adjusted. The advantageous effect that the contrast of the input image can be adjusted is obtained by performing the weighted average synthesis based on different coupling coefficients.

According to the image processing method of the nineteenth aspect, in the image processing method of the fifteenth aspect, the contrast adjusting step converts the pixel value in the input image into a target signal to be a target for contrast adjustment. Signal conversion step, an initial setting step for setting an initial condition and a pixel comparison range when calculating a contrast adjustment amount for the converted target signal, and a contrast of each pixel for the target signal converted based on the pixel comparison range. An object correction information deriving step for obtaining the adjustment amount, an end determination step for determining whether or not the contrast adjustment processing based on the obtained contrast adjustment amount of each pixel is completed in all pixel comparison ranges, and an end determination step for the end determination step If not, the correction range that changes the pixel comparison range and passes the process to the target correction information derivation step If an end step is determined in the updating step and the end determination step, an extraction step is performed to limit the effective range from the contrast adjustment amount of each pixel, and conversion is performed in the limited range based on the contrast adjustment amount of each pixel. The pixel value in the input image is adjusted by the target signal conversion step of converting the target signal to the adjusted target signal to generate the adjusted target signal, and the adjusted target signal and the target signal converted in the signal conversion step. And a signal inverse conversion step of performing an inverse conversion process into a value, so that the contrast adjustment amount calculated by comparing the target pixel value with the average pixel value in the peripheral region is a weighted average of the contrast adjustment amounts in a plurality of peripheral regions. Since the value can be expanded to the value, the advantageous effect that the highly accurate contrast adjustment can be performed is obtained.

According to the image processing method of the twentieth aspect, in the image processing method of the sixteenth aspect or the seventeenth aspect, the pixel value converting step includes an average luminance calculating step of calculating an average luminance in the input image, Based on the calculated average brightness, the conversion method classification step that selects the conversion method when converting the pixel value in the input image to the adjusted pixel value based on the obtained contrast adjustment amount, and the conversion method classification step According to the conversion method, it has a pixel value estimation step of converting the obtained contrast adjustment amount into a pixel value of an actual pixel, which is considered effective from the contrast adjustment amount obtained by comparison with surrounding pixels. After extracting the area, it is possible to select and apply a conversion method for converting the extracted contrast adjustment amount based on the average brightness in the input image. , Advantageous effect can be performed easily and reliably contrast adjustment can be obtained.

According to the image processing method of the twenty-first aspect, in the image processing method of the eighteenth or nineteenth aspect, the target signal converting step is an average value of the target signals obtained in the signal converting step in the input image. And an average target signal calculation step for generating an average target signal, and based on the generated average target signal, the target signal obtained based on the contrast adjustment amount of the obtained target signal is converted into an adjustment target signal. A conversion method classification step of selecting a conversion method when performing, and a target signal estimation step of converting the obtained contrast adjustment amount of the target signal into an actual pixel target signal value according to the conversion method obtained in the conversion method classification step. After extracting a region that seems to be effective from the contrast adjustment amount of the target signal (for example, brightness) obtained by comparison with surrounding pixels by having , It is possible to select and apply a conversion method for converting the contrast adjustment amount of the extracted target signal based on the average target signal value (average brightness) in the input image, so that the contrast adjustment is performed easily and surely. The advantageous effect that it can be obtained is obtained.

According to the image processing method of the twenty-second aspect, in the image processing method of the sixteenth aspect or the seventeenth aspect, the pixel value conversion step is a criterion for calculating a reference intensity value indicating a contrast intensity in the input image. An intensity calculation step, based on the calculated reference intensity value, a conversion curve estimation step of estimating a conversion curve when converting the pixel value in the input image to the adjusted pixel value based on the obtained contrast adjustment amount, By using the estimated conversion curve and a pixel value estimation step of converting the obtained contrast adjustment amount into the pixel value of the actual pixel, it is effective from the contrast adjustment amount obtained by comparison with surrounding pixels. It is possible to calculate the sum of the edge strengths obtained from the brightness of the input image after extracting the likely region, so convert the extracted contrast adjustment amount. When multiple candidate conversion curves are prepared, the total edge strength of the luminance of the final output candidate image obtained from each conversion curve is calculated, and the ratio of that value to the total edge strength of the input image is used as the evaluation value for optimum conversion. The advantageous effect is that the curve can be estimated with a genetic algorithm.

According to the image processing method of the twenty-third aspect, in the image processing method of the eighteenth or nineteenth aspect, the target signal converting step is performed in the input image with respect to the target signal obtained in the signal converting step. The reference intensity calculation step of calculating the reference intensity value indicating the contrast intensity of By having a target signal conversion curve estimation step of estimating a conversion curve when performing, and a target signal estimation step of converting the obtained target signal into an adjustment target signal using the estimated conversion curve, Target signal (brightness) obtained by comparison After extracting an area that seems to be effective from the contrast adjustment amount, the total of the edge strength of the input image is calculated from the target signal of the input image. It is possible to calculate It is possible to obtain the advantageous effect that the optimum conversion curve can be estimated by the genetic algorithm by calculating and using the ratio of the calculated value to the total edge strength of the input image as an evaluation value.

According to the image processing method of the twenty-fourth aspect, in the image processing method of the twenty-second aspect, the conversion curve estimating step includes an adjustment vector of an adjustment vector composed of a preset fixed number of adjustment parameters. Pixel value conversion that converts the pixel value in the input image into the adjusted pixel value based on the obtained contrast adjustment amount using the initial candidate setting step of setting the initial candidate group and each vector in the adjustment vector group at the present time A candidate calculation step, an evaluation value calculation step of evaluating the contrast strength by each conversion curve candidate using the converted pixel value obtained from the reference intensity value and each vector in the adjustment vector group at the present time,
Generate a new adjustment vector set by performing a recomposition operation on the current candidate based on the goodness-of-fit calculation step that calculates the goodness-of-fit of each candidate obtained in the evaluation value derivation step in the previous term and the calculated goodness-of-fit of each candidate. By including the recombination operation step for performing the adjustment operation and the estimation end determination step for determining whether or not the optimization of the adjustment vector is completed, the area considered to be more effective than the contrast adjustment amount obtained by comparison with the surrounding pixels. Since it is possible to calculate the sum of the edge intensities obtained from the brightness of the input image after extracting, a plurality of conversion curves for converting the extracted contrast adjustment amount are prepared and obtained from each conversion curve. Calculate the total edge strength of the brightness of the final output candidate image, and use the ratio of that value to the total edge strength of the input image as the evaluation value to determine the optimal conversion curve. Advantageous effect that it is possible to estimate the rhythm is obtained.

According to the image processing method of the twenty-fifth aspect, in the image processing method of the twenty-third aspect, the target signal conversion curve estimating step is an adjustment composed of a preset fixed number of adjustment parameters. Using the initial candidate setting step of setting the initial candidate group of vectors and each vector in the adjustment vector group at the present time, the obtained target signal is set as the adjustment target signal based on the contrast adjustment amount of the obtained target signal. Using the target signal conversion candidate calculation step for conversion and the target signal value after conversion obtained from each vector in the adjustment vector group at the present time and the reference intensity value, the contrast intensity of the target signal by each conversion curve candidate is evaluated. The evaluation value calculation step and the fitness calculation step that calculates the fitness of each candidate obtained in the evaluation value derivation step A recombination operation step that creates a new adjustment vector set by performing a recombination operation on the current candidate based on the goodness of fit of each candidate, and an estimation end determination that determines whether the optimization vector of the adjustment vector has finished By including a step and extracting a region that seems to be more effective than the target signal (brightness) contrast adjustment amount obtained by comparison with surrounding pixels, the sum of the edge strength of the input image is calculated from the target signal of the input image Therefore, multiple conversion curves for converting the contrast adjustment amount of the extracted target signal are prepared, and the total edge strength of the target signal of the final output candidate image obtained from each conversion curve is calculated. , It is advantageous that the optimal transformation curve can be estimated by the genetic algorithm using the ratio of that value to the total edge strength of the input image as the evaluation value. Results can be obtained.

According to the image processing method of the twenty-sixth aspect, in the image processing method of the fifteenth aspect, the color adjusting step includes an image dividing step of dividing the input image into a plurality of small areas, and an image dividing step. A representative color extraction step for obtaining a representative color representative of the color data in each small area obtained in step 1, a color conversion table that collects color data after color conversion prepared in advance, and a representative color obtained in the representative color extraction step. Based on the distance between the representative color and the conversion color obtained in the representative color conversion color selection step, the color closest to the color is selected as the conversion color from the color conversion table, and the selected color is selected. The input image is divided into a plurality of detailed regions by finely adjusting each converted color and converting the representative color into the converted color after the fine adjustment, and the statistical distribution of the colors in each region is obtained. Representative color The color in the color conversion table prepared in advance is compared with the representative color to extract the converted color that is the closest to each representative color, and based on the distance between the two colors. The selected color data after the conversion can be finely adjusted, and the representative color in each area can be converted into the finely adjusted conversion color, and the image after the color adjustment thus obtained and the input image can be appropriately combined. The weighted average synthesis based on the coefficient has an advantageous effect that the color of the input image can be adjusted.

According to the image processing method of the twenty-seventh aspect, in the image processing method of the twenty-sixth aspect, the representative color extracting step performs sequential division processing on the color data in the divided small areas. Set the start state group when performing, classify all the color data into each group, calculate the representative color of each group, and the target group based on the distribution of the color data that belongs to the division target group. A division axis determination step of determining a component of interest at the time of division, and a cluster division step of dividing the target group into a plurality of groups according to the obtained focus component and allocating color data belonging to the target group to groups obtained after the division. , A cluster representative color determination step for obtaining a representative color of the color data belonging to each obtained group, and a judgment as to whether or not the representative colors have converged. , If it is not converged, the process moves to the cluster division step to perform group division again based on the current representative color, and if it is determined that it has converged in the convergence determination step, Determine whether a predetermined number of representative colors from the target area obtained so far have been obtained, and if not, move to the division axis determination step Cluster division end determination step and cluster division When it is determined that the image is finished in the end determination step, the input image is divided into a plurality of detail areas by having a representative color output step of outputting the obtained predetermined number of representative colors, and Since the representative color can be selected based on the statistical distribution of colors, the colors in the color conversion table prepared in advance are compared with each other, and the converted color closest to each representative color is extracted. The distance between two colors to fine-tune the color data after the conversion, which is selected based,
The representative color in each area can be converted into this finely adjusted converted color, and the input image is obtained by performing a weighted average synthesis of the image after color adjustment thus obtained and the input image based on an appropriate coupling coefficient. The advantageous effect that the color adjustment can be performed is obtained.

According to the image processing method of the twenty-eighth aspect, in the image processing method of any one of the fifteenth to twenty-seventh aspects, the image synthesizing step is performed after adjusting the input image and the image processing step. Based on the selection reference value determination step that determines which of the images of (1) and (2) is prioritized, the coupling coefficient that is applied to the input image and the adjusted image obtained in the image processing step is determined based on the determination result of the selection reference value determination step. A weighted average synthesizing step for generating a weighted average image of the input image and the adjusted image obtained in the image processing step using the determined coupling coefficient derivation step and the coupling coefficient of each image determined in the coupling coefficient derivation step By having the following, the contrast adjustment amount is obtained by comparing the pixel value of the target pixel with the average pixel value in the surrounding area, and the Since the last adjustment amount can be extracted, the contrast adjustment of the input image can be performed by performing the weighted average synthesis of the thus obtained contrast adjusted image and the input image based on an appropriate coupling coefficient. An advantageous effect is acquired.

[Brief description of drawings]

1 is a first embodiment of the present invention, 1, 2, 3, 4, 5, 6 ,;
Block diagram showing the basic configuration of the image processing apparatus according to 7, 8 and 9

2A is a block diagram showing the image processing means of FIG. (B) Block diagram showing the image processing means of FIG.

FIG. 3 is a block diagram showing a contrast adjusting means of FIG.

FIG. 4 is a block diagram showing a contrast adjusting means.

FIG. 5 is a block diagram showing a contrast adjusting means.

FIG. 6 is a block diagram showing a contrast adjusting means.

FIG. 7A is a block diagram showing pixel value conversion means. (B) Block diagram showing target signal converting means

FIG. 8A is a block diagram showing pixel value conversion means. (B) Block diagram showing target signal converting means

FIG. 9A is a block diagram showing a conversion curve estimating means prepared for genetic algorithm estimation. FIG. 9B is a block diagram showing a target signal conversion curve estimating means prepared for genetic algorithm estimation.

FIG. 10 is a block diagram showing the image synthesizing means of FIG.

FIG. 11 is an explanatory diagram schematically showing human vision.

FIG. 12 is a flowchart showing the operation of the contrast adjusting means.

FIG. 13 is a flowchart showing the operation of the image synthesizing means of the image processing apparatus according to the first embodiment of the present invention.

FIG. 14 is an explanatory diagram schematically showing a peripheral visual field region.

FIG. 15 is a flowchart showing the operation of the contrast adjusting means.

FIG. 16 is a flowchart showing the operation of the contrast adjusting means.

FIG. 17 is a flowchart showing the operation of the contrast adjusting means.

FIG. 18 is a flowchart showing the operation of the pixel value changing means.

FIG. 19 is a flowchart showing the flow of processing in the contrast adjusting means.

FIG. 20 is a flowchart showing a processing flow of a conversion curve estimation means in contrast adjustment processing.

FIG. 21 is a schematic diagram schematically showing a chromosome structure used in a genetic algorithm in the conversion curve estimation means.

FIG. 22 is a schematic diagram schematically showing an outline of a roulette selection method in the conversion curve estimation means.

FIG. 23 is a schematic diagram schematically showing the recombining operation process of the genetic algorithm used in the conversion curve estimating means.

FIG. 24 is a block diagram showing a color adjusting unit.

FIG. 25 is a flowchart showing a color adjustment process in a color adjustment unit.

FIG. 26 is a block diagram showing representative color extracting means.

FIG. 27 is a flowchart showing the processing by the representative color extracting means.

FIG. 28 is a block diagram showing an image processing device in Document 1.

FIG. 29 is a block diagram showing an imaging device in Document 2.

FIG. 30 is an explanatory diagram of a digital image improving method in Document 3.

[Explanation of symbols]

10 Image input means 11 Image processing means 12 Image synthesizer 13 Image output means 20 Contrast adjustment means 21 Color adjustment means 30 Correction information deriving means 31 Extraction means 32 pixel value conversion means 40 Initial setting means 41 end determination means 42 Correction range changing means 50 Signal conversion means 51 Target correction information deriving means 52 target signal conversion means 53 Signal inverse conversion means 70 Average brightness calculation means 71 Conversion method classification means 72 Pixel value estimating means 73 Average Target Signal Calculation Means 74 Target signal estimating means 80 Reference strength calculation means 81 Conversion Curve Estimating Means 82 Target Signal Conversion Curve Estimating Means 90 Initial candidate setting means 91 pixel value conversion candidate calculation means 92 Evaluation value deriving means 93 Fitness calculation means 94 Recombination operation means 95 Estimated end determination means 100 selection reference value determination means 101 coupling coefficient deriving means 102 weighted average synthesizing means 240 image segmentation means 241 Representative color extraction means 242 Representative color conversion color selection means 243 color conversion table 244 color conversion means 260 initialization means 261 division axis determining means 262 cluster dividing means 263 cluster representative determining means H.264 convergence determination means 265 cluster division end determination means 266 Representative color output means

─────────────────────────────────────────────────── ─── Continuation of front page (51) Int.Cl. ⁷ Identification code FI theme code (reference) H04N 5/20 H04N 1/40 101E 9/64 D (72) Inventor ▲ Yasuhiro Kuwahara Kadoma City, Osaka Prefecture Daiji Kadoma 1006 Matsushita Electric Industrial Co., Ltd. (72) Inventor Yusuke Monobe, Kadoma City, Osaka Prefecture 1006 Kadoma Matsushita Electric Industrial Co., Ltd. (72) Toshiharu Kurosawa Dai Kadoma 1006, Kadoma City Osaka Prefecture In-house F-term (reference) 5B057 BA02 BA25 BA29 CA01 CA08 CA12 CA16 CB01 CB08 CB12 CB16 CC02 CE08 CE11 CE17 CE18 CH07 DA17 DB02 DB06 DB09 DC22 DC25 5C021 PA56 PA71 PA76 XA35 5C066 AA11 EA13 HA03 KD07 KE77 BA06C07 A0711 MP01 MP08 NN02 PP15 PP23 PP32 PP49 PQ12 PQ18 PQ19 PQ23

Claims (28)

[Claims] 1. An image input means for converting an input analog image signal into digital image data, an image processing means for performing desired image processing on the converted digital image data to generate processed image data, and the processing. Image processing means for generating combined image data by combining the completed image data with the digital image data, and image output means for converting the combined image data into an analog combined image signal, the image processing means The image processing apparatus is a contrast adjusting means for performing contrast adjustment on the digital image data or a color adjusting means for performing color adjustment on the digital image data. 2. Contrast adjusting means, correction information deriving means for obtaining a contrast adjusting amount of a target pixel, extracting means for limiting and extracting an effective range from the obtained contrast adjusting amount of the target pixel, The image processing apparatus according to claim 1, further comprising a pixel value conversion unit that converts a pixel value of the target pixel into an adjusted pixel value in the limited range based on a contrast adjustment amount of the pixel. 3. The contrast adjusting means sets an initial condition for calculating a contrast adjustment amount of a target pixel and a pixel comparison range, and a contrast of the target pixel based on the pixel comparison range. Correction information derivation means for obtaining an adjustment amount, end determination means for determining whether or not the contrast adjustment processing based on the obtained contrast adjustment amount of the target pixel is completed in all the pixel comparison ranges, and the end determination means. If the end determination is not made, the correction range changing means for changing the pixel comparison range and passing the process to the correction information deriving means, and if the end determination means makes a plurality of the pixel comparison ranges, Extraction means for limiting and extracting the effective range from the obtained contrast adjustment amount, and the above-mentioned limitation based on the contrast adjustment amount of the target pixel. The image processing apparatus according to claim 1, characterized in that it comprises a pixel value converting means for converting the pixel value of the target pixel to the adjusted pixel values in ranges. 4. The contrast adjusting means converts the pixel value in the input image into a target signal to be a target for contrast adjustment, and a target pixel for the target signal obtained by the signal converting means. Correction amount deriving means for obtaining the contrast adjustment amount of the target pixel, extraction means for limiting and extracting an effective range from the contrast adjustment amount of the target pixel obtained by the target correction information deriving means, and the contrast adjustment amount of the target pixel A target signal converting means for converting the target signal obtained by the signal converting means into an adjustment target signal in the limited range to generate an adjusted target signal; and the adjusted target signal and the signal conversion. And a signal inverse conversion unit that performs an inverse conversion process on the pixel value in the input image into an adjusted pixel value according to the target signal obtained by the unit. The image processing apparatus according to claim 1. 5. The signal processing means for converting the pixel value in an input image into a target signal to be a target for contrast adjustment, and an initial stage for calculating a contrast adjustment amount for the converted target signal. Initial setting means for setting a condition and a pixel comparison range, target correction information deriving means for obtaining a contrast adjustment amount of each pixel with respect to the converted target signal based on the pixel comparison range, and the obtained contrast of each pixel An end determination unit that determines whether or not the contrast adjustment processing based on the adjustment amount is completed in all the pixel comparison ranges; and if the end determination unit does not determine the end, the pixel comparison range is changed. Correction range changing means for passing processing to the target correction information deriving means, and the contra of each pixel when the end determination means determines the end Extraction means for limiting and extracting an effective range from the adjustment amount, and an adjusted target by converting the converted target signal into an adjustment target signal in the limited range based on the contrast adjustment amount of each pixel. Target signal conversion means for generating a signal, and signal inverse conversion means for performing inverse conversion processing of pixel values in the input image into adjusted pixel values by the adjusted target signal and the target signal converted by the signal conversion means. The image processing apparatus according to claim 1, further comprising: 6. The pixel value converting means calculates an average luminance in the input image, and an average luminance calculating means, and based on the calculated average luminance, the input image based on the obtained contrast adjustment amount. Of the actual pixel has the obtained contrast adjustment amount according to a conversion method classification unit that selects a conversion method when converting the pixel value in the pixel value into an adjusted pixel value, and the conversion method obtained by the conversion method classification unit The image processing apparatus according to claim 2, further comprising a pixel value estimating unit that converts the pixel value. 7. The target signal converting means calculates an average value of the target signals obtained by the signal converting means in an input image, and generates an average target signal.
Based on the generated average target signal, a conversion method classification means for selecting a conversion method when converting the obtained target signal to the adjustment target signal based on the contrast adjustment amount of the obtained target signal, 5. A target signal estimation means for converting the obtained contrast adjustment amount of the target signal into an actual target signal value of a pixel according to the conversion method obtained by the conversion method classification means. 5. The image processing device according to item 5. 8. The pixel value converting means calculates a reference intensity value indicating a contrast intensity in an input image, and the obtained contrast adjustment amount based on the calculated reference intensity value. Based on the conversion curve estimating means for estimating the conversion curve when converting the pixel value in the input image into the adjusted pixel value, and using the estimated conversion curve, the obtained contrast adjustment amount to the actual pixel 4. The image processing apparatus according to claim 2, further comprising a pixel value estimating unit that converts the pixel value into a pixel value of the image processing apparatus. 9. The target signal converting means calculates a reference strength value indicating a contrast strength in an input image with respect to the target signal obtained by the signal converting means, and the calculated reference strength. A target signal conversion curve estimating means for estimating a conversion curve when converting the obtained target signal into an adjustment target signal based on the obtained contrast adjustment amount of the target signal; The image processing apparatus according to claim 4, further comprising a target signal estimating unit that converts the obtained target signal into the adjustment target signal using a conversion curve. 10. The conversion curve estimating means sets an initial candidate group of an adjustment vector composed of a preset fixed number of adjustment parameters, and each vector in the adjustment vector group at the present time. Using,
Obtained from pixel value conversion candidate calculation means for converting the pixel value in the input image into an adjusted pixel value based on the obtained contrast adjustment amount, and the reference intensity value and each vector in the adjustment vector group at the present time. Evaluation value calculation means for evaluating the contrast intensity by each conversion curve candidate using the converted pixel value, fitness calculation means for calculating the fitness of each candidate obtained by the evaluation value derivation means, A recombination operation means for generating a new adjustment vector set by performing a recombination operation on the current candidate based on the calculated goodness of fit of each candidate, and an estimation for judging whether or not the optimization vector of the adjustment vector is completed The image processing apparatus according to claim 8, further comprising an end determination unit. 11. The target signal conversion curve estimating means includes an initial candidate setting means for setting an initial candidate group of adjustment vectors constituted by a preset fixed number of adjustment parameters, and an adjustment vector group at the present time. Target signal conversion candidate calculation means for converting the obtained target signal into an adjustment target signal based on the obtained contrast adjustment amount of the target signal using each vector, the reference intensity value and the current adjustment By using the target signal value after conversion obtained from each vector in the vector group, evaluation value calculation means for evaluating the contrast strength of the target signal by each conversion curve candidate, and each obtained by the evaluation value derivation means A new adjustment vector is obtained by performing a refitting operation on the current candidate based on the relevance calculating means for calculating the relevance of the candidate and the relevance calculated for each candidate. The image processing apparatus according to claim 9, characterized in that it comprises a recombinant manipulation means for generating a set, the estimated completion determining means for determining whether the time optimization is complete adjustment vector. 12. The color adjusting means divides an input image into a plurality of small areas, and a representative color for obtaining a representative color representative of color data in each small area obtained by the image dividing means. Extraction means, a color conversion table in which color data after color conversion prepared in advance is collected, and a representative color conversion that selects a color closest to the representative color obtained by the representative color extraction means as a conversion color from the color conversion table Based on the distance between the representative color obtained by the color selection means and the representative color conversion color selection means and the conversion color, each of the selected conversion colors is finely adjusted, and the representative color is finely adjusted. The image processing apparatus according to claim 1, further comprising a color conversion unit that converts the color into a later color. 13. The representative color extracting means sets a group in a start state when performing sequential division processing on color data in a divided small area, classifies all color data into each group, Initializing means for obtaining a representative color of each group, division axis determining means for determining a component of interest when dividing the target group based on the distribution of color data belonging to the division target group, and the obtained focus A cluster dividing unit that divides the target group into a plurality of groups according to the components, and distributes the color data belonging to the target group to groups obtained after the division, and a cluster for obtaining a representative color of the color data belonging to each of the obtained groups. The representative color determination means determines whether or not the representative colors have converged. If the representative colors have not converged, group division is performed again based on the current representative color. If the convergence determination means that the processing shifts to the cluster division means and the convergence determination means determines whether or not a predetermined number of representative colors from the target area obtained so far have been obtained If it is determined that it has not been obtained, and if it is determined by the cluster division end determination means that the processing has been completed, the processing to the division axis determination means is performed. The image processing apparatus according to claim 12, further comprising a representative color output unit that outputs a number of representative colors. 14. The image synthesizing means determines a selection reference value determining means for determining which of an input image and an adjusted image obtained by the image processing means is prioritized, and the selection reference value determining means. Based on the result, a coupling coefficient deriving means for determining a coupling coefficient applied to the input image and the adjusted image obtained by the image processing means, and a coupling coefficient of each image determined by the coupling coefficient deriving means are used. 14. A weighted average synthesizing means for generating a weighted average image of the input image and the adjusted image obtained by the image processing means, according to any one of claims 1 to 13. Image processing device. 15. An image input step of converting an input analog image signal into digital image data, an image processing step of performing desired image processing on the converted digital image data to generate processed image data, and the processing. And an image output step of converting the composite image data into an analog composite image signal, the image processing step comprising: combining the processed image data with the digital image data to generate composite image data; Is a contrast adjustment step of performing contrast adjustment on the digital image data or a color adjustment step of performing color adjustment on the digital image data. 16. The contrast adjusting step comprises: a step of deriving correction information for obtaining a contrast adjusting amount of a target pixel; an extracting step of limiting and extracting an effective range from the obtained contrast adjusting amount of the target pixel; 16. The image processing method according to claim 15, further comprising a pixel value conversion step of converting a pixel value of the target pixel into an adjusted pixel value in the limited range based on a contrast adjustment amount of a pixel. 17. The contrast adjusting step includes an initial setting step of setting an initial condition and a pixel comparison range when calculating a contrast adjustment amount of the target pixel, and a contrast of the target pixel based on the pixel comparison range. A correction information derivation step of obtaining an adjustment amount; an end determination step of determining whether or not the contrast adjustment processing based on the obtained contrast adjustment amount of the target pixel has ended in all the pixel comparison ranges; and an end determination step. If the end determination is not made, a correction range changing step of changing the pixel comparison range and passing the process to the correction information deriving step; An extraction step of extracting a limited effective range from the obtained contrast adjustment amount; and the target pixel The image processing method according to claim 15, characterized in that it comprises a pixel value conversion step of converting the pixel value of the target pixel to the adjusted pixel values in the limited range based on the contrast adjustment amount. 18. The contrast adjustment step comprises a signal conversion step of converting a pixel value in an input image into a target signal to be a target for contrast adjustment, and a target pixel for the target signal obtained in the signal conversion step. A target correction information derivation step of obtaining a contrast adjustment amount of the target pixel, an extraction step of limiting and extracting an effective range from the contrast adjustment amount of the target pixel obtained in the target correction information derivation step, and a contrast adjustment amount of the target pixel A target signal conversion step of converting the target signal obtained in the signal conversion step into an adjusted target signal in the limited range to generate an adjusted target signal, and the adjusted target signal and the signal conversion A signal for performing a reverse conversion process on the pixel value in the input image into an adjusted pixel value based on the target signal obtained in step The image processing method according to claim 15, characterized in that it comprises a conversion step. 19. The contrast adjusting step comprises a signal converting step of converting a pixel value in an input image into a target signal to be a target for contrast adjustment, and an initial step in calculating a contrast adjustment amount for the converted target signal. An initial setting step of setting a condition and a pixel comparison range, a target correction information deriving step of obtaining a contrast adjustment amount of each pixel with respect to the converted target signal based on the pixel comparison range, and a contrast of the obtained pixel An end determination step of determining whether or not the contrast adjustment processing based on the adjustment amount is completed in all the pixel comparison ranges,
A correction range changing step of changing the pixel comparison range and passing the processing to the target correction information deriving step when the end determination step does not determine the end; An extraction step of limiting and extracting a range effective from the contrast adjustment amount of each pixel, and adjusting the converted target signal into an adjustment target signal in the limited range based on the contrast adjustment amount of each pixel A target signal conversion step of generating an adjusted target signal, and a signal inverse for performing an inverse conversion process of a pixel value in the input image into an adjusted pixel value by the adjusted target signal and the target signal converted in the signal conversion step. And a conversion step.
5. The image processing method according to item 5. 20. The pixel value converting step calculates an average brightness in the input image, and based on the calculated average brightness, the input image is calculated based on the obtained contrast adjustment amount. The conversion method classification step of selecting a conversion method when converting the pixel value in the pixel value into the adjusted pixel value, and the obtained contrast adjustment amount of the actual pixel according to the conversion method obtained in the conversion method classification step. The image processing method according to claim 16, further comprising a pixel value estimation step of converting the pixel value. 21. An average target signal calculating step of calculating an average value of the target signals obtained in the signal converting step in an input image to generate an average target signal in the target signal converting step, and the generated average target. A conversion method classification step of selecting a conversion method when converting the obtained target signal into an adjustment target signal based on the signal based on the contrast adjustment amount of the obtained target signal, and the conversion method classification step 20. A target signal estimation step of converting the obtained contrast adjustment amount of the target signal into an actual target signal value of a pixel according to the conversion method obtained in step 20. Processing method. 22. The pixel value converting step includes a reference intensity calculating step of calculating a reference intensity value indicating a contrast intensity in an input image, and the calculated reference intensity value based on the calculated reference intensity value.
Based on the obtained contrast adjustment amount, a conversion curve estimating step of estimating a conversion curve when converting a pixel value in the input image into an adjusted pixel value, and the obtained using the estimated conversion curve. The image processing method according to claim 16, further comprising a pixel value estimation step of converting the contrast adjustment amount into a pixel value of an actual pixel. 23. The target signal converting step, a reference strength calculating step of calculating a reference strength value indicating a contrast strength in an input image with respect to the target signal obtained in the signal converting step, and the calculated reference strength. Based on the value, based on the amount of contrast adjustment of the obtained target signal,
A target signal conversion curve estimation step of estimating a conversion curve when converting the obtained target signal to an adjustment target signal, and converting the obtained target signal to the adjustment target signal using the estimated conversion curve. The image processing method according to claim 18, further comprising a target signal estimation step. 24. The conversion curve estimating step comprises an initial candidate setting step of setting an initial candidate group of adjustment vectors composed of a preset fixed number of adjustment parameters, and each vector in the adjustment vector group at the present time. Based on the obtained contrast adjustment amount,
Using the pixel value conversion candidate calculation step of converting the pixel value in the input image into the adjusted pixel value, and the converted pixel value obtained from the reference intensity value and each vector in the adjustment vector group at the present time Based on the evaluation value calculation step for evaluating the contrast intensity by each conversion curve candidate, the compatibility calculation step for calculating the compatibility of each candidate obtained in the evaluation value derivation step, and the calculated compatibility of each candidate. And a recombination operation step of generating a new adjustment vector set by performing a recombination operation of the current candidate, and an estimated end determination step of determining whether or not the optimization of the adjustment vector is completed. The image processing method according to claim 22. 25. The step of estimating a target signal conversion curve comprises:
An initial candidate setting step of setting an initial candidate group of adjustment vectors configured by a preset fixed number of adjustment parameters, and using each vector in the adjustment vector group at the present time, the contrast of the obtained target signal A target signal conversion candidate calculation step of converting the obtained target signal into an adjustment target signal based on the adjustment amount, and the converted target obtained from the reference intensity value and each vector in the adjustment vector group at the present time. Using the signal value and
An evaluation value calculating step for evaluating the contrast intensity of the target signal by each conversion curve candidate, a fitness calculating step for calculating the fitness of each candidate obtained in the evaluation value deriving step, and a fitness of each calculated candidate A recombination operation step of generating a new adjustment vector set by performing a recombination operation of the current candidate based on the above, and an estimation end determination step of determining whether or not the optimization of the adjustment vector is finished. The image processing method according to claim 23, wherein: 26. The color adjusting step includes an image dividing step of dividing an input image into a plurality of small areas, and a representative color for obtaining a representative color representative of color data in each small area obtained in the image dividing step. Extraction step, a color conversion table that collects color data after color conversion prepared in advance, and a representative color conversion that selects a color closest to the representative color obtained in the representative color extraction step as a conversion color from the color conversion table Based on the color selection step and the distance between the representative color and the converted color obtained in the representative color conversion color selection step, each of the selected conversion colors is finely adjusted, and the representative color is finely adjusted. The image processing method according to claim 15, further comprising a color conversion step of converting into a subsequent conversion color. 27. The representative color extracting step sets a group in a start state when performing sequential division processing for color data in a divided small area, classifies all color data into each group, An initialization step of obtaining a representative color of each group, a division axis determination step of determining a component of interest when dividing the target group based on a distribution of color data belonging to the division target group, and the obtained focus According to the component, while dividing the target group into a plurality,
A cluster division step of allocating color data belonging to the target group to groups obtained after division, a cluster representative color determination step of obtaining a representative color of the color data belonging to each of the obtained groups, and whether the representative colors have converged If it has not converged, it is determined that convergence has occurred in the convergence determination step in which the process moves to the cluster division step to perform group division again based on the current representative color, and the convergence determination step. If judged,
A determination is made as to whether or not a predetermined number of representative colors from the target area obtained so far have been obtained, and if not obtained, a cluster division end determination step in which the processing to the division axis determination step shifts, 27. The image processing method according to claim 26, further comprising: a representative color output step of outputting a predetermined number of obtained representative colors when it is determined that the cluster division end determination step is completed. 28. In the image synthesizing step, a selection reference value determining step that determines which of an input image and the adjusted image obtained in the image processing step is prioritized, and determination of the selection reference value determining step. Based on the result, a coupling coefficient derivation step of determining a coupling coefficient applied to the input image and the adjusted image obtained in the image processing step, and a coupling coefficient of each image determined in the coupling coefficient derivation step are used. And a weighted average combining step of generating a weighted average image of the input image and the adjusted image obtained in the image processing step.
The image processing method according to any one of 5 to 27.