JP4635884B2 - Image processing apparatus and method - Google Patents

Description

  The present invention is based on a density calculation processing unit that obtains an arithmetic mean value of RGB components of each pixel of input original image data as an input pixel density value, and an input pixel density value calculated and derived by the density calculation processing unit. The present invention relates to an image processing apparatus including a density correction processing unit that performs density correction so that predetermined gradation characteristics can be obtained, and a method thereof.

  In general, a photographic image processing apparatus is an image provided with a media drive or the like for inputting image data taken by a digital photographing device such as a film scanner or a digital still camera that digitizes a photographed image formed on a photographic film as original image data. An input unit, an image processing unit that performs various image processing such as density correction and color correction on the original image data, and exposing a photosensitive material (photographic paper) based on print data converted after the image processing A print processing unit for generating a photographic print is provided.

  The above-mentioned image processing unit is suitable for human visual characteristics in consideration of differences in gradation characteristics of input image data and gradation characteristics of photosensitive materials in order to obtain photographic prints with good image quality. Density correction processing for matching is performed.

  As such density correction processing, correction processing is generally performed so that input image data and output image data have a density characteristic curve exhibiting a predetermined gamma characteristic, and a density histogram of the entire target input image is obtained. A density correction curve is generated based on the reference density correction value obtained from the above, and the input image data is converted based on the density correction curve.

  As described in Patent Document 1, the pixel density value that is the basis of such correction has been obtained as an arithmetic average value of the RGB components of each pixel constituting the original image data.

JP 2005-159387 A

  However, the conventional arithmetic mean value of the RGB components of each pixel for images with high-saturation subjects such as scenes of greens such as forests and scenes of red and yellow flowers. When the density correction is performed so that a predetermined gradation characteristic is obtained based on the input pixel density value, the gradation value of a specific color is high, but the gradation value of other colors is low. Lower.

  Since the image corrected by the density correction curve generated based on the reference density correction value obtained from the density histogram based on the input pixel density value is processed as an under-exposure image, the image has a low overall density. There is a problem that a preferable print finish cannot be obtained.

  Therefore, if correction is performed using a density correction curve generated based on a reference density correction value obtained from a density histogram in which the maximum RGB component value is set to the density value of each pixel for each pixel, an image obtained by photographing a highly saturated subject is obtained. The print finish will be good, but if this method is applied to all the images, for example, the face of a person photographed in a backlight scene will become darker depending on the scene. There was an adverse effect that the print finish was poor.

  In view of the above-described conventional problems, an object of the present invention is to provide an image processing apparatus and an image processing method capable of obtaining an appropriate pixel density value corresponding to an input original image and appropriately correcting the density.

In order to achieve the above object, the first characteristic configuration of the image processing apparatus according to the present invention is the RGB component of each pixel of the input original image data as described in claim 1 of the document of the claims. A density calculation processing unit that obtains an arithmetic mean value as an input pixel density value, and a density correction process that corrects the density so as to obtain a predetermined gradation characteristic based on the input pixel density value calculated and derived by the density calculation processing unit The density calculation processing unit is a weighting factor set for each predetermined region on the hue saturation table to which each pixel belongs, and the outer region is higher in saturation. The maximum RGB component value (Max (R, G, B)) and the additive RGB component are added according to [Equation 4] based on the weighting factor (r _h ) that is set smaller for the inner region that is larger and lower in saturation. Average value (Avr (R, G, B) It lies in calculating the weighted average value of the as input pixel density value.

  According to the above configuration, in a region where the gradation value of a specific color is high in the original image, the input pixel density value is obtained by weighting the maximum RGB component value, and the gradation value of the specific color in the original image is obtained. In a region where the saturation is not high, weighting is performed so as to obtain the input pixel density value by weighting the arithmetic average value of the RGB components, thereby maintaining the pixel density of the subject with high saturation while maintaining the high saturation. It is possible to perform density correction to an appropriate pixel density value in a portion other than a high subject.

  In addition, by performing the processing by weighting, the ratio of applying the maximum value of the RGB component is increased (decreased) step by step with respect to the change in the saturation level, the change in the position of the hue with respect to the primary color region, and the like. Since the ratio at which the arithmetic average value of RGB components is applied can be decreased (increased) stepwise, an unnatural change in pixel density value between adjacent neighboring pixels due to the difference in the type of calculation applied It is possible to prevent this.

  As described in claim 2, the second feature configuration includes, in addition to the first feature configuration described above, a face area detection unit that detects a face area of a subject included in the original image data, and the original image. A backlight scene determination unit that determines whether or not the data is a backlight scene, and when the face region is detected by the face region detection unit and is determined to be a backlight scene by the backlight scene determination unit, The density calculation processing unit is to obtain an arithmetic mean value of RGB components of each pixel of the original image data as an input pixel density value.

  If the subject is a backlight scene including a face area, etc., the correction for the backlight scene is a good correction in which the saturation of the backlight scene is high, but the reproduction of the face area is inevitably darkened. End up. This is different from a scene where the green color of a forest including a high-saturation area is simply taken.On the contrary, the correction to reduce the density makes it suitable for human visual characteristics, so the subject includes a face area. In addition, in the case of a backlight scene, the facial region may be subjected to density correction to an appropriate pixel density value by obtaining an arithmetic mean value of RGB components of each pixel of the original image data as an input pixel density value. It becomes possible.

In order to achieve the above object, the first characteristic configuration of the image processing method according to the present invention, as described in claim 3, is to input an arithmetic average value of RGB components of each pixel of the input original image data. A density calculation processing step for obtaining a pixel density value; and a density correction processing step for correcting the density so as to obtain a predetermined gradation characteristic based on the input pixel density value calculated and derived by the density calculation processing step. An image processing method comprising:
The density calculation processing step is a weighting factor set for each predetermined region on the hue saturation table to which each pixel belongs, and is set larger for the outer region with higher saturation and smaller for the inner region with lower saturation. Based on the weighting factor (r _h ), the maximum RGB component value (Max (R, G, B)) and the arithmetic average value of RGB components (Avr (R, G, B)) according to [Equation 5] and Is calculated as the input pixel density value .

  The second feature configuration includes a face region detection step of detecting a face region of a subject included in the original image data in addition to the first feature configuration described above, and the original image as described in claim 4. A backlight scene determination step for determining whether the data is a backlight scene, a face region is detected by the face region detection step, and when the scene is determined to be a backlight scene by the backlight scene determination step, The arithmetic processing step is that an arithmetic mean value of RGB components of each pixel of the original image data is obtained as an input pixel density value.

In the third feature configuration, in addition to the second feature configuration described above, the density correction processing step includes a density based on the input pixel density value calculated in the density calculation processing step. A face-dependent density correction value model that is set to a larger value as the correction value Gamma and the average density value of the face area detected by the face area detection unit are smaller, and to a smaller value as the average density value of the face area is larger. The face-dependent density correction value Gamma (F) calculated by the equation is derived by [Equation 6] based on the face area weight coefficient r _f that becomes larger as the ratio of the face area to the entire original image data increases. In accordance with the appropriate density correction value Gamma (R), a density correction curve for density correction for the original image data is generated.

  According to the above-described configuration, the density correction value obtained from the characteristics of the entire input image and the face-dependent density correction value based on the average density value of the face area are merged, so that not only the entire image but also the face of the person who is the subject. When obtaining an appropriate density correction value also taking into account the area, the reference density correction value and the face-dependent density correction value are weighted by a weighting factor based on the average density value of the face area and the face area size, and the appropriate density correction value is obtained. Since the density correction value is corrected, the degree of fusion is adjusted based on the ratio of the face area in the input image and the average density, and appropriate density correction can be performed for the entire image and the face area of the subject. It will be possible.

  As described above, according to the present invention, it is possible to provide an image processing apparatus and an image processing method capable of obtaining an appropriate pixel density value corresponding to an input original image and appropriately correcting the density. It was.

  Embodiments of a photographic image processing apparatus incorporating an image processing apparatus according to the present invention will be described below. As shown in FIG. 2, the photographic image processing apparatus 1 includes a photographic printer 2 that performs an exposure process on the photographic paper P based on output image data and develops the exposed photographic paper, and a developed photographic film F. A media driver 32 that reads image data from an image data storage medium M such as a memory card that stores image data taken by a film scanner 31 or a digital still camera that reads an image from the camera, a general-purpose computer as a controller 33, and the like The print data is set and inputted with the print order information for the inputted photographic image as the original image, and includes an operation station 3 for performing various image correction processes, and the print data edited from the original image by the operation station 3 Is output to the photographic printer 2 to produce a desired photographic print. That.

  As shown in FIGS. 2 and 3, the photographic printer 2 has two systems of photographic paper magazines 21 containing roll-shaped photographic paper P, and photographic paper P drawn from the photographic paper magazine 21 with a predetermined print. A sheet cutter 22 that cuts into a size; a back print unit 23 that prints print information such as a frame number on the back of the cut photographic paper P; an exposure unit 24 that exposes the photographic paper P based on the print data; A development processing unit 25 including a plurality of processing tanks 25a, 25b, and 25c filled with processing solutions for developing, bleaching, and fixing the exposed photographic paper P is disposed along the transport path of the photographic paper P. The laterally-feeding conveyor 26 that discharges the photographic paper P that has been dried after the development process, and the sheet-paper (photo print) P that is stacked on the laterally-feeding conveyor 26 is sorted in order units. Configured to include the data 27.

  The exposure unit 24 receives a laser beam bundle of RGB three colors modulated based on the print data in the main scanning direction orthogonal to the conveyance direction with respect to the photographic paper P conveyed in the sub-scanning direction by the conveyance mechanism 28. An exposure head 24a for outputting and exposing is accommodated.

  A transport mechanism 28 including a plurality of roller pairs that transport the photographic printing paper P at a process speed corresponding to the exposure unit 24 and the development processing unit 25 disposed along the transport path is disposed before and after the exposure unit 24. Is provided with a chucker-type transport mechanism 28a capable of transporting photographic paper P in multiple rows.

  The controller 33 provided in the operation station 3 is installed with an application program that operates under the control of a general-purpose operating system and executes various controls of the photographic processing apparatus 1, and a monitor 34 as an operation interface with the operator. A keyboard 35, a mouse 36, and the like are connected.

  The photographic processing process executed by the cooperation of the hardware and software of the controller 33 will be described in functional blocks. As shown in FIG. 4, photographic image data read by the film scanner 31 and the media driver 32 is received. An image input unit 40 that performs predetermined preprocessing and transfers it to a memory 41, which will be described later, and print order information and image editing information are displayed on the screen of the monitor 34, and an operation for inputting necessary data for them. A graphic user interface unit 42 that generates a graphic operation screen for displaying an icon or generates various control commands based on an input operation from the keyboard 35 or mouse 36 to the displayed graphic operation screen, and the image input Photos transferred from the section 40 A memory 41 in which image data and photographic image data after correction processing by an image processing unit 47 (to be described later), correction parameters at that time, and set print order information are stored in a predetermined area, and print order information An order processing unit 43 for generating image data, an image processing unit 47 for performing density correction processing and contrast correction processing for each photographic image data stored in the memory 41, and display commands from the graphic user interface unit 42. A display control unit 46 having a video RAM for displaying the image data and various input / output graphic data developed in the memory 41 on the monitor 34 and the final correction after the various correction processes are completed. Print data for generating print data for outputting an image to the photo printer 2 A generating unit 44, and the like formatter 45 which converts the final corrected image according to customer orders to a file format for writing in a storage medium such as a CD-R.

  The film scanner 31 is configured to operate in two modes: a pre-scan mode that reads an image recorded on the film F at a high speed although it is a low resolution, and a main scan mode that reads a high resolution at a low speed. Various correction processes described later are performed on the low-resolution image read in the can mode, and the high-resolution image read in the main scan mode based on the correction parameters stored in the memory 41 at that time. A final correction process is executed and output to the printer 2.

  Similarly, the image file read from the media driver 32 includes a high-resolution captured image and its thumbnail image, and various correction processes described later are performed on the thumbnail image. Based on the stored correction parameters, a final correction process is performed on the high-resolution captured image. When the image file does not include a thumbnail image, the image input unit 40 generates a thumbnail image from the high-resolution captured image and transfers it to the memory 41. In this way, the calculation load of the controller 33 is reduced by executing various editing processes that are frequently trial and error on low-resolution images.

  As shown in FIG. 1, the image processing unit 47 includes a density calculation processing unit 11 that obtains an arithmetic average value (R + G + B) / 3 of RGB components of each pixel of input original image data as an input pixel density value; A face area detection unit 12 that detects a face area of a subject included in the original image data, a backlight scene determination unit 13 that determines whether or not the original image data from which the face area is detected is a backlight scene, Based on the input pixel density value calculated and derived by the density calculation processing unit 11, the density correction processing unit 14 corrects the density so as to obtain a predetermined gradation characteristic, and the density correction processing unit 14 generates the density correction. A contrast correction unit 15 that performs contrast correction on the density correction curve, a distortion correction unit 16 that corrects distortion caused by the photographing lens, and a sharpening process that enhances the edges of the image and reduces noise. 17, a color correction processing unit 18 is configured to include a scaling processing section 19 for converting the size image size suitable for photographic prints.

  The density calculation processing unit 11 includes a hue saturation weighting coefficient setting unit 110 that sets a weighting coefficient (hereinafter referred to as a hue saturation weighting coefficient) for each predetermined area on the hue saturation table (HueSat table), and the hue. The weighted average value of the RGB component maximum value and the RGB component arithmetic average value based on the saturation weighting coefficient is calculated for each pixel of the input original image, and the calculated weighted average value is used as the density correction processing unit. 14 and a weighted average density calculation unit 111 that is obtained as an input pixel density value to 14.

  The hue saturation weight coefficient setting unit 110 divides the hue saturation table as shown in FIG. 5 into a plurality of areas, and sets the hue saturation weight coefficient for each divided area. Then, for each pixel of the input original image, it is determined which region of the hue / saturation table the pixel belongs to, and the hue / saturation weighting used when calculating a weighted average value to be described later Determine the coefficient. The hue saturation weighting coefficient indicates the usage ratio of the RGB component maximum value or the RGB component arithmetic average value in the calculation of the weighted average value in the weighted average density calculation unit 111 to be described later. In the example, the hue saturation weighting coefficient will be described as the usage ratio of the maximum value of RGB components.

  For example, the hue saturation weighting coefficient setting unit 110 is an area where the hue saturation weighting coefficient is increased and the saturation is low as the area outside the hue saturation table is a high saturation area. The hue saturation weighting coefficient is set to be smaller as the inner area of the hue saturation table. That is, as shown in FIG. 6A, the hue saturation weighting coefficient is 1 in the area a1, the hue saturation weighting coefficient is 0.75 in the area a2, and the hue saturation is in the area a3. If the hue weighting coefficient is 0.5, the hue saturation weighting coefficient is 0.25 in the area a4, and the hue saturation weighting coefficient is 0 in the area a5, the above structure is satisfied. .

  Further, the hue saturation weighting coefficient is increased as the hue is in the vicinity of each primary color and each complementary color, and the hue saturation weighting coefficient is decreased as the hue is in the intermediate area between each primary color and each complementary color. It may be a configuration. For example, as shown in FIG. 6B, the hue saturation weighting coefficient is 1 in the area b1, the hue saturation weighting coefficient is 0.5 in the area b2, and the hue saturation is in the area b3. If the degree weighting coefficient is set to 0, the above configuration is satisfied.

  Furthermore, as shown in FIG. 6 (c), the hue saturation weighting factor considering both saturation and hue is set by performing region division combining FIG. 6 (a) and FIG. 6 (b). It may be configured to.

The weighted average density calculation unit 111 calculates a weighted average value of the RGB component maximum value and the RGB component arithmetic average value based on the hue saturation weight coefficient for each pixel of the input original image, The calculated weighted average value of each pixel, that is, the average density value of each pixel is obtained as an input pixel density value to the density correction processing unit 14. That is, the weighted average value is calculated by performing the weighting calculation as shown in [Equation 7] for each pixel of the input original image.

Here, r _h is the hue saturation weighting coefficients used for the pixel data, R is the pixel value of the red component in the pixel data, G is a pixel value of the green component in the pixel data, B is The pixel value of the blue component in the pixel data, Max (R, G, B) is the maximum pixel value of the RGB component for the pixel data, and Avr (R, G, B) is the RGB value for the pixel data. The arithmetic average value of the components, and Avr (r _h ) is a weighted average value for the pixel data.

The density calculation processing unit 11 detects that a face region is detected from the original image data in a face region detection unit 12 described later, and the original image data is a backlight scene in a backlight scene determination unit 13 described later. If determined, the arithmetic average value of the RGB components is calculated for each pixel of the input original image regardless of the position of the hue saturation table of each pixel, and the arithmetic average of the calculated pixels is calculated. A value, that is, an average density value of each pixel is obtained as an input pixel density value to the density correction processing unit 14. That is, the weighting calculation by the above [Equation 7] is not executed.

  The face area detection unit 12 includes a face area extraction unit 120 that extracts a human face area from the original image data, and a face area average density calculation unit 121 that calculates an average density based on the extracted pixel data of the face area. And a face area size calculation unit 122 that calculates the face area size based on the number of pixels of the extracted face area.

  For example, the face area extraction unit 120 determines whether or not a contour based on a density edge or a color edge extracted from the original image data is a face area. It is realized using a known technique such as detection based on a pattern recognition technique by evaluating the degree of coincidence with a plurality of element arrangement patterns such as ears. Note that the extraction process in the face area extraction unit 120 is not only configured to be automatically performed as described above, but the operator manually operates the original image displayed on the screen of the monitor 34 with the mouse 36 or the like. It may be configured to extract the area designated by the input as a face area.

The face area average density calculation unit 121 derives an arithmetic average value of RGB components for each pixel constituting the face area extracted by the face area extraction unit 120, and further, based on the derived average value of each pixel. An average value of all pixels constituting the face area is obtained as an average density value D _Favr of the face area.

  The face area size calculation unit 122 is configured to calculate the number of pixels of the face area data extracted by the face area extraction unit 120 as a face area size. In addition, when a plurality of face areas are extracted from the original image in which a plurality of persons are photographed by the face area extraction unit 120, the total number of pixels in each extracted face area is calculated as each face area size. At the same time, the total number of pixels of all the extracted face areas is calculated as the overall face area size.

  The backlight scene determination unit 13 determines whether or not the original image data is a backlight scene. More specifically, the backlight scene determination unit 13 determines the original scene when the difference between the density of the face area calculated by the face area average density calculation unit 121 and the density of the area other than the face area exceeds a predetermined value. It is configured to determine that the image data is a backlight scene.

  The density correction processing unit 14 sets a density correction value 140 based on the input pixel density value of the entire input original image, and the original image according to the density correction value or an appropriate density correction value described later. Based on a density correction curve generation unit 141 that generates a density correction curve for correcting the density of the data, and a face-dependent density correction value model expression using the average density value of the face area detected by the face area detection unit 12 as a variable. A face-dependent density correction value setting unit 142 for deriving a face-dependent density correction value, a face area weight coefficient calculating unit 143 for calculating a weight coefficient based on the average density value and the face area size of the face area, and the density correction value And an appropriate density correction value deriving unit 1 for deriving an appropriate density correction value by weighting the face dependent density correction value with a weighting factor based on the average density value of the face area and the face area size. It is constituted by a 4.

The density correction value setting unit 140 generates a density histogram in the original image data, and sets the density correction value Gamma of the original image data in accordance with a function stored in advance [Equation 8] .

  Here, as shown in FIG. 7, the Maxg is the maximum density value of the original image data, that is, the density value of the brightest pixel, and the Ming is the density value of the original image data. The minimum value, that is, the darkest pixel density value, and the Mass is a density value that is an average density of the original image data, that is, an average density value of each pixel calculated by the weighted average density calculation unit 111. . The Maxk is the maximum value of the density setting value in the original image data. For example, when the density value is set between 0 gradation and 255 gradation in 8 bits, Maxk is set to 255. The Mink is the minimum density setting value in the original image data. For example, the Mink is set to 0 when the density value is set between 0 gradation and 255 gradation in 8 bits. That is, the color reproduction density of the photographic print image is the maximum at the minimum value 0 of the density setting value, and the color reproduction density is the minimum at the maximum value 255. The green area of the forest, the light area of the backlight scene, etc. are gradation areas close to the maximum value 255.

  The density correction value Gamma can be configured to be appropriately set via the keyboard 35 or mouse 36 by an operator who visually determines the original image displayed on the monitor 34.

The density correction curve generation unit 141 is stored in advance based on the density correction value Gamma when the face area detection unit 12 determines that a human face area is not detected in the subject. A density correction curve embodied as a lookup table that converts the input pixel density value Nin (i) into an output pixel density value Nout (i) corresponding to the density correction value Gamma according to the function shown in [Equation 9]. (I = 0, 1, 2,..., 255 (in the case of 8-bit data)).

  Here, the constant L is a target gradation setting width after density correction processing of the original image data. For example, when the output image data is set by 8-bit data, the target gradation setting width is 255. The output pixel density value Nout (i) is output as a value from 0 to 255.

  That is, as shown in FIG. 8, when the density correction value setting unit 140 sets the density correction value Gamma to a value greater than 1, the density correction curve generation unit 141 makes the original image data brighter. A density correction curve for density correction processing is generated, and when the density correction value Gamma is set to a value smaller than 1, a density correction curve for density correction processing is generated so that the original image data becomes dark. Has been.

  In the above description, the case where the face area detection unit 12 determines that a person's face area is not detected in the subject has been described. However, it is determined that a person's face area has been detected in the subject. In this case, the density correction curve generation unit 141 is configured to generate a density correction curve based on an appropriate density correction value Gamma (R) obtained by an appropriate density correction value deriving unit 144 described later.

  The face-dependent density correction value setting unit 142 is a face-dependent density correction value Gamma (F) based on a face-dependent density correction value model equation using the average density value of the face area detected by the face area detection unit 12 as a variable. Is derived. The model formula visually evaluated a correction image based on a density correction curve based on a density correction value obtained by changing the reference density correction value for a large number of sample images obtained by shooting various subjects under various shooting conditions. As a result, the correction function F1 is obtained by statistically processing the distribution of density correction values determined to have corrected the average density of the face area and the face area at that time to an appropriate density.

FIG. 9 shows an example of the correction function F1, and the face-dependent density correction value Gamma (F) is expressed as Gamma (F) = F1 (D _Favr ) with respect to the average density value D _Favr of the face region. It is required. Here, the average density of the face region is shown as a value normalized with an 8-bit image.

  As can be understood from FIGS. 8 and 9, when the average density of the face region becomes small, that is, darkens, the face-dependent density correction value Gamma (F) becomes a value larger than 1, and such face-dependent density correction value Gamma. According to the density correction curve generated based on (F), the density correction processing is performed so that the original image data becomes brighter, and the average density of the face area increases, that is, the face-dependent density correction value Gamma (F ) Becomes a value smaller than 1, and according to the density correction curve generated based on such face-dependent density correction value Gamma (F), density correction processing is performed so that the original image data becomes dark. It becomes a characteristic.

  The face area weight coefficient calculation unit 143 uses an average density value of the face area to be used when the density correction value and the face-dependent density correction value are merged in an appropriate density correction value derivation unit 144 described later. And a weighting factor (hereinafter referred to as a fusion rate) based on the face area size.

For example, in the input original image, the detected face areas are two face areas A and B, the sizes of the face areas are S _A and S _B , respectively, and the size of the entire original image is S _In the case of _sum , the ratio P occupied by the face region is calculated by performing an operation such as [Equation 10] .

Then, the fusion rate r _f is calculated by performing an operation such as [Equation 11] on the ratio P occupied by the face region.

Here, the coefficients C and D are values that can be set as appropriate within the effective range of the present embodiment, and a predetermined value is stored as a result of trial and error. It is a value that is set as appropriate based on populations with different imaging conditions (for example, the type, intensity, direction, exposure amount, etc. of imaging light). Specific values of the coefficients C and D include, for example, C = 0.05, D = 0... Under the condition that the weight coefficient r _f based on the average density value and the face area size exceeds 1, When 5 is set, the fusion rate r _f increases from 0.5 to 1 as the ratio P occupied by the face region increases from 0 to 100.

When the ratio of the face area in the whole original image is large by using the fusion rate r _f , the face-dependent density correction is performed when the density correction value and the face-dependent density correction value are fused. It is possible to place a weight on the value.

The appropriate density correction value deriving unit 144 corrects the density correction value Gamma by performing a weighting operation on the density correction value Gamma and the face-dependent density correction value Gamma (F) with the fusion rate r _f. An appropriate density correction value Gamma (R) is calculated, and the original image is corrected by the density correction curve generated by the density correction curve generation unit 141 based on the appropriate density correction value Gamma (R). Thus, the face area of the person who is the main subject is corrected so as to show a preferable gradation.

More specifically, the appropriate density correction value deriving unit 144 is based on the reference density correction value Gamma, the face-dependent density correction value Gamma (F), and the fusion rate r _f as shown in [Equation 12]. The density correction value Gamma is corrected to an appropriate density correction value Gamma (R) by performing a weighting calculation.

  Based on the appropriate density correction value Gamma (R) obtained by the appropriate density correction value deriving unit 144 in this way, the contrast correction processing unit is applied to the density correction curve generated by the density correction curve generating unit 141. 15 performs contrast correction.

The contrast correction processing unit 15 outputs, for example, the input pixel density value Nin (i) of the original image data according to the contrast correction values Cr1 and Cr2 according to [Equation 13] and [ Equation 14] stored in advance. A contrast correction curve is generated as a lookup table for converting the density value to the pixel density value Cout (i).

Specifically, according to [Equation 13], with respect to the highlight area of the original image data in which the input pixel density value Nin (i) is equal to or greater than a predetermined density threshold Nth as shown in FIG. The original image in which the input pixel density value Nin (i) is less than or equal to a predetermined density threshold value Nth as shown in FIG. 10B is generated according to [Equation 14] . A contrast correction curve for generating a contrast correction process for the shadow area of the data is generated.

  The contrast correction values Cr1 and Cr2 are calculated based on the density correction value Gamma or the appropriate density correction value Gamma (R) set by the density correction processing unit 14, and as the contrast correction value Cr1 is larger, It becomes a look-up table for correcting the contrast of the highlight area to be high, and becomes a look-up table for correcting the contrast of the shadow area to be higher as the contrast correction value Cr2 is negatively larger.

  The contrast correction processing unit 15 performs a fusion process on the generated contrast correction curve and the density correction curve, and generates a fusion curve as a lookup table capable of executing the density correction process and the contrast correction process simultaneously. In other words, as shown in FIG. 11, when the original image data is input as an input pixel density value Nin (i), the fusion curve has an output pixel density value Cout (Nout) via the density correction curve and the contrast correction curve. (I)) is generated as a lookup table for density value conversion.

  Hereinafter, density correction processing by the image processing apparatus of the present invention will be described based on the flowchart of FIG.

  When the thumbnail image data read in the prescan mode by the film scanner 31 or read from the media driver 32 is input to the memory 41 (S1), the face area extracting unit 120 in the face area detecting unit 12 Then, a process of extracting a face area from the original image data is executed (S2).

  At this time, when a face area is detected in the original image data in the face area extraction unit 120 (S3), the backlight scene determination unit 13 determines whether or not the original image data is a backlight scene. When the original image data is determined to be a backlight scene (S4), the weighted average density calculation unit 111 calculates the arithmetic average value of the RGB components of each pixel as the average density value of each pixel, The average density value of each pixel is calculated for all pixel data in the original image data (S5).

The density correction value setting unit 140 generates a density histogram in the original image data based on the average density value calculated in step S5, and the maximum density value of the original image data, that is, the brightest pixel. a density value Maxg of the minimum value of the density values the original image data has, i.e., calculates the density value Ming darkest pixel, and a density value Mass as the average density of the original image data, the [Equation 7 ] , The density correction value Gamma is calculated (S6).

  Further, in the face area detection unit 12, based on the detected face area data, the face area average density calculation unit 121 calculates the average density value of the face area, and the face region size calculation unit 122 calculates the face area size. (S7).

  Subsequently, the face-dependent density correction value setting unit 142 calculates a face-dependent density correction value Gamma (F) based on the average density value of the face area (S8), and the face area weight coefficient calculation unit 143 A weighting factor (fusion rate) based on the average density value of the face area and the face area size is calculated (S9).

  Then, the appropriate density correction value deriving unit 144 performs a weighting operation on the density correction value Gamma and the face-dependent density correction value Gamma (F) with the fusion rate (S10), thereby obtaining the density correction value Gamma. The proper density correction value Gamma (R) is calculated by correction (S11).

  On the other hand, if a face area is not detected in step S3 or if it is determined that the scene is not a backlight scene in step S4, the hue / saturation weight coefficient setting unit 110 uses it for each pixel of the input original image. A hue saturation weighting coefficient is determined (S12), and the weighted average density calculation unit 111 calculates a weighted average value of the RGB component maximum value and the RGB component arithmetic average value based on the hue saturation weight coefficient of each pixel. The average density value of each pixel is calculated, and the average density value of each pixel is calculated for all pixel data in the original image data (S13).

Subsequently, the density correction value setting unit 140 generates a density histogram in the original image data based on the average density value calculated in step S13, that is, the maximum density value of the original image data, that is, Calculating a density value Maxg of the brightest pixel, a minimum density value of the original image data, that is, a density value Ming of the darkest pixel, and a density value Mass that is an average density of the original image data; A density correction value Gamma is calculated based on [Equation 7] (S14).

  In the density correction curve generation unit 11, when a face area is detected in the original image, based on the appropriate density correction value Gamma (R) calculated in step 11, and in the original image If no area is detected, a density correction curve is generated based on the density correction value Gamma calculated in step 14 (S15). Subsequently, the contrast correction processing unit 15 applies the density correction curve to the density correction curve. Then, a density correction curve in which the contrast correction curves are merged is generated (S16), density correction processing of the original image data is performed based on the density correction curve, and the result is displayed on the monitor 34 (S17). .

  Thus, the density correction curve obtained in step S16 is stored in the memory 41 as a density correction parameter, and is then read in the main scan mode by the film scanner 31 or read from the media driver 32. Density correction is performed on the resolution image data by the stored density correction parameter.

Hereinafter, another embodiment will be described. In the above-described embodiment, the face area average density calculating unit 121 calculates the average value of all the pixels constituting the face area based on the arithmetic average value of the RGB components for each pixel constituting the face area. The configuration _obtained as the average density value D _Favr has been described. However, the face area average density calculation unit 121 detects that a plurality of face areas are detected by the face area extraction unit 120, that is, there are a plurality of faces in the subject. If the average density value of the face area is calculated for each of a plurality of face areas, a weighting operation is performed based on the size of the detected face area, thereby calculating the average of the entire face area in the subject. The density value may be calculated as the face area weighted average density value _DWavr .

For example, in the input original image, the detected face areas are two face areas A and B, the sizes of the face areas are S _A and S _B , respectively, and the average density value of the face area is In the case of D _A and D _B , the face area weighted average density value D _Wavr is calculated by performing an operation such as [ _Equation 15] .

Incidentally, in the face region average density calculating section 121, when calculating the face area weighted mean density value _{D WAVR} instead of the average density value _{D Favr} of the face region, in deriving the face dependent density correction value Gamma (F) It is configured to use a face region weighted mean density value D _WAVR instead of the average density value D _Favr of the face region.

  In the above-described embodiment, the configuration has been described in which the backlight scene determination unit 13 determines whether the original image data is a backlight scene based on the difference between the density of the face area and the density of the area other than the face area. However, the determination as to whether or not the backlight scene determination unit 13 is a backlight scene is based on the fact that the original image data is a backlight scene when the difference between the density of the central portion of the image and the density of the peripheral portion exceeds a predetermined value. It may be configured to determine.

  Note that the above-described embodiment is merely an example of the present invention, and it is needless to say that the specific configuration and the like of each block can be changed and designed as appropriate within the scope of the effects of the present invention.

Explanatory drawing of functional block configuration in image processing unit Explanatory drawing of the external configuration of the photographic image processing apparatus Illustration of photo printer Explanatory drawing of functional block configuration of photographic image processing apparatus Explanatory diagram of hue saturation table In setting a hue saturation weighting coefficient in each divided area of the hue saturation table, (a) is divided by saturation, (b) is divided by hue, and (c) is divided by saturation and hue. Explanatory drawing of the divided hue saturation table Explanatory diagram of density histogram related to density correction value calculation Explanatory drawing of density value transition in density correction processing Explanatory drawing of the graph which is an example of the model formula for deriving the face dependent density correction value (A) shows the contrast correction curve in the highlight area, and (b) is an explanatory diagram showing the contrast correction curve in the shadow area. Illustration of fusion curve Flow chart for explaining the operation of the image processing unit

1: Photo image processing device 47: Image processing unit 11: Density calculation processing unit 110: Hue / saturation weighting coefficient setting unit 111: Weighted average density calculation unit 12: Face area detection unit 13: Backlight scene determination unit 14: Density correction processing Unit 142: Face-dependent density correction value setting unit 143: Face area weight coefficient calculation unit 144: Appropriate density correction value derivation unit

Claims (5)

A density calculation processing unit that obtains an arithmetic mean value of RGB components of each pixel of the input original image data as an input pixel density value, and a predetermined floor based on the input pixel density value calculated and derived by the density calculation processing unit. An image processing apparatus including a density correction processing unit that performs density correction so as to obtain a tonal characteristic,
The density calculation processing unit is a weighting factor set for each predetermined region on the hue saturation table to which each pixel belongs, and is set larger for an outer region with higher saturation and smaller for an inner region with lower saturation. Based on the weighting factor (r _h ), according to [Equation 1], the maximum value of RGB components (Max (R, G, B)) and the arithmetic average value of RGB components (Avr (R, G, B)) and An image processing apparatus that calculates the weighted average value of the input pixel density values .
  A face area detection unit that detects a face area of a subject included in the original image data; and a backlight scene determination unit that determines whether or not the original image data is a backlight scene. When an area is detected and the backlight scene determination unit determines that the scene is a backlight scene, the density calculation processing unit uses an arithmetic average value of RGB components of each pixel of the original image data as an input pixel density value. The image processing apparatus according to claim 1 to be obtained. A density calculation processing step for obtaining an arithmetic average value of RGB components of each pixel of the input original image data as an input pixel density value, and a predetermined floor based on the input pixel density value calculated and derived by the density calculation processing step. An image processing method comprising a density correction processing step for correcting density so as to obtain a tone characteristic,
The density calculation processing step is a weighting factor set for each predetermined region on the hue saturation table to which each pixel belongs, and is set larger for the outer region with higher saturation and smaller for the inner region with lower saturation. Based on the weighting coefficient (r _h ), the maximum value of RGB components (Max (R, G, B)) and the arithmetic average value of RGB components (Avr (R, G, B)) according to [Equation 2] An image processing method for calculating a weighted average value as an input pixel density value .
A face area detecting step for detecting a face area of a subject included in the original image data; and a backlight scene determining step for determining whether or not the original image data is a backlight scene. When an area is detected and the backlight scene is determined to be a backlight scene by the backlight scene determination step, the density calculation processing step uses an arithmetic average value of RGB components of each pixel of the original image data as an input pixel density value. The image processing method according to claim 3 to be obtained. The density correction processing step is set to a larger value as the density correction value Gamma based on the input pixel density value calculated in the density calculation processing step and the average density value of the face area detected by the face area detection unit become smaller. The ratio of the face area to the total of the original image data is the face-dependent density correction value Gamma (F) calculated by the face-dependent density correction value model formula that is set to a smaller value as the average density value of the face area increases. A density correction curve for correcting the density of the original image data is generated according to the appropriate density correction value Gamma (R) derived from [Equation 3] based on the face area weighting factor r _f that increases as the value of the face area increases. The image processing method according to claim 4.