JP4998794B2 - Image correction method and image correction apparatus - Google Patents

Description

  The present invention relates to an image correction technique for correcting an input image into a proper image using a correction curve.

  In the field of photographic print output, which is a typical field for correcting input images to appropriate images, photographic images (data) obtained by digitizing photographic images formed on photographic film using a film scanner, and digital cameras After image correction such as density correction and color correction is performed on the shot image (data) obtained by digitizing the shot image directly with a digital shooting device such as this, it is converted to print data, and a photo is taken based on this print data The mainstream is a digital photo processing technique in which a print unit is driven and a photographed image is printed on a photosensitive material (printing paper) with a light beam or formed on a printing paper with color ink. In order to obtain an appropriate shot image under various photography conditions, it is necessary to appropriately set shooting conditions such as shutter speed, aperture setting, weather, and light source type. The actual situation is that the photographer is not consciously aware of this, and an inappropriate photographed image in which the color tone or contrast of the subject is not properly obtained is input to the photographic printing apparatus. There are many things. The main image corrections used to reproduce the acquired input captured image as an appropriate output captured image, such as a photographic print, are color correction, density correction, and contrast correction. These corrections are adjusted and combined correctly. Image correction is required. A correction often used in the field of photographic prints is a basic correction curve (gamma curve) created from the relationship between an input photographed image that is empirically and experimentally known and an image of a photographic print that is output based on this image. The final correction curve is created by adjusting the partial shape and overall inclination of the basic correction curve according to the image characteristics of each input captured image. The input captured image is corrected based on the above. However, when the adjustment of the basic correction curve is artificially performed, there is a problem that it takes skill and labor, and when it is automatically performed, the finished quality is insufficient.

  Recently, neural network technology has been applied to image correction. For example, the selected representative color RGB signal is used as input data, and the above representative color duplicate image output by an output device (printer) is used. The RGB signal read again by the input device (scanner) is used as teacher data to construct a neural network, and by inputting each pixel value of the input image to the input layer, through the intermediate layer, the output layer There is an image processing apparatus that outputs an ink amount of a printer from the printer and thereby obtains a good duplicate image (see, for example, Patent Document 1). Furthermore, a neural network is provided that has previously learned an image signal as input data and an image correction amount as a teacher value, and the image signal is corrected using the image correction amount obtained by inputting the image signal to the neural network. An image forming apparatus that obtains a stable output image is also known (see, for example, Patent Document 2). However, image correction using a neural network outputs the result determined by statistical empirical rules using a computer, so if an image that does not resemble the sample image used when constructing the neural network is input There is a possibility that an irregular correction result is output. In particular, when this correction technique is applied to the photographic print field, the commercial value is lost when the face of a person most important for photographic print becomes irregular.

Japanese Patent Laid-Open No. 09-168095 (paragraph numbers 0050-0057, 0066, FIG. 4) Japanese Unexamined Patent Publication No. 09-018716 (paragraph numbers 0039-0040, FIG. 1)

  In view of the above situation, an object of the present invention is to provide an image correction technique for correcting an input image to an appropriate image using a correction curve output using a statistical learning rule represented by a neural network. Regardless of whether or not the input image includes a human face, the correction of the face area is satisfactory, and an image correction technique that can maintain harmony with areas other than the face is provided. .

  In an image correction method for correcting an input image to a proper image using a correction curve, in order to solve the above-described problem, the method of the present invention is characterized in that an image feature of an image sequentially selected as an original image from a number of prepared sample images. A step of constructing a statistical learning rule part having a quantity as an input value and learning a reference correction curve for creating a proper image from the original image as a teacher value; a sample image with a face from which a face region is detected from the sample image; Extracting an appropriate image with a face corresponding to the sample image with face; calculating a regression equation from a reference density value of the face region in the sample image with face and a reference density value of the face region in the image with face; Obtaining an image feature amount group from the input image; and inputting the image feature amount group of the input image to the statistical learning rule unit. A step of creating a reference correction curve based on an output value output from the statistical learning rule part, a step of calculating a reference density value of a face area detected from the input image as an input density value, An execution correction curve by determining an output density value derived from the input density value using a regression equation and adjusting the reference correction curve so that a relationship between the input density value and the output density value is established. And the step of correcting the input image with the execution correction curve.

  In the above method, a statistical learning rule part is constructed in advance from a large number of sample images and appropriate images obtained by appropriately correcting the sample images, and corresponding to the reference density value of the face area of the sample image whose face has been detected. A regression equation is calculated by performing regression analysis on the reference density value of the face area of the appropriate image. When an image to be actually corrected is input, a basic density curve is output by inputting the image feature amount group of the input image to the statistical learning rule part, but a face is included in the input image, When the face area is detected, the relationship between the reference density value serving as a reference for the pixel density value included in the face area and the output density value obtained from the regression equation using the reference density value as the input density value The basic density curve is adjusted so that is established. By correcting the input image using the basic density curve adjusted in this way, optimization is made with emphasis placed on the face area in the entire area of the input image, and the basic density curve is also applied to the entire input image. Since the correction is made with the shape of, a harmonious correction is realized.

  Images handled in the photographic print industry are color images. Therefore, in a preferred embodiment of the present invention, the correction curve and the regression equation are created for each luminance value calculated from the R density value, the G density value, the B density value, and the R / G / B density value. As a result, more fine correction can be performed. Of course, luminance values (for example, (R) are calculated from R, G, and B density values without creating a correction curve for each of R, G, and B density values (C, M, and Y density values are also synonymous). The correction curve and the regression equation may be created only from the density value + G density value + B density value) / 3)), or the correction curve for each of the R, G, B density values and the correction curve for the luminance value and the regression. An expression may be created.

  It is preferable to adopt the average density value of the face area as the reference density value of the face area. Through experimental consideration, it has been found that the average density value is a value that easily and accurately represents the image characteristics of the face region. Moreover, since it was confirmed that the correlation is linear with high reliability when a general sample image with a face and its appropriate image are subjected to regression analysis, it is also preferable to adopt a regression line as a regression equation.

  In an image correction apparatus that corrects an input image to a proper image using a correction curve, in order to solve the above-described problem, the apparatus according to the present invention is characterized in that an image feature of an image sequentially selected as an original image from a number of prepared sample images. A statistical learning rule part constructed by learning a reference correction curve for creating a proper image from the original image as a teacher value and using a quantity as an input value, and a sample image with a face from which a face region extracted from the sample image is detected A regression equation table that stores a regression equation calculated from a reference density value of a face region in the appropriate image with a face corresponding to the sample image with the face, an image feature amount calculation unit that obtains an image feature amount group from the input image, By inputting an image feature amount group of the input image to the statistical learning rule unit, a reference correction music is generated based on an output value output from the statistical learning rule unit. Derived from the input density value using the regression equation table, a reference correction curve generation section for creating a reference density curve, a first preprocessing section for calculating a reference density value of the face area detected from the input image as an input density value, A second pre-processing unit that determines an output density value to be performed, and a correction curve adjustment unit that generates an execution correction curve by adjusting the reference correction curve so that the relationship between the input density value and the output density value is established And an image correction unit that corrects the input image with the execution correction curve. Naturally, this image correction apparatus can also obtain all the effects described in the above image correction method, and can further incorporate the above-described additional techniques.

  Statistical learning rules used in the field of image processing include conjugate gradient method, genetic algorithm, neural network, etc., but hardware or software is becoming more modular, easy to handle, and 10,000 Considering that it is also easy to train using a teacher value created from a large number of samples exceeding the above, a neural network is advantageous. Therefore, in the image correction method and the image correction apparatus according to the present invention, it is preferable to employ a neural network unit as the statistical learning rule unit.

  The present invention also includes an image correction program for causing a computer to execute the above-described image correction method using the correction curve. Other features and advantages of the present invention will become apparent from the following description of embodiments using the drawings.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. First, a reference correction curve (based on an image feature amount group obtained from an image to be corrected, which is an important component of the correction technique according to the present invention (more Specifically, the construction principle of a neural network unit will be described as an example of a statistical learning rule unit that generates a coefficient that defines a correction curve.

  First, as schematically shown in FIG. 1, a large number of images acquired under various shooting conditions are prepared as sample images, and these sample shot images are used as original images, through display on a monitor and test prints. Image correction is manually performed so that an appropriate image is obtained (# 01). Next, by comparing and evaluating the pixel values of the original image and the appropriate image obtained by manual image correction, an image correction amount that defines the correspondence between the pixel value of the original image and the pixel value of the appropriate image, and consequently Generates a manual correction curve defined by the image correction amount (# 02).

Next, an image feature amount is calculated from the sample image that is the original image (# 03). As the image feature amount, a region-independent image feature amount obtained from a density histogram generated from the sample image or a region-dependent feature amount in a region group divided from the sample image can be employed. Region dependent feature amount is the average density value in the parts-domain of the image, the maximum density value, a feature amount such as minimum density value, as the region independent feature groups, the average of the entire image density, maximum density value, minimum The density value , the density gradation value , and the integrated frequency value for each partial region.

  Since the coefficient defining the correction curve for each density region obtained in step # 02 and the image feature quantity group obtained in step # 03 are used as learning data for constructing the neural network unit, step # The operations from 01 to step # 03 are performed for all the prepared sample images. Then, iterative simulation that adjusts the internal variables of each neural network so that the coefficient that defines the correction curve of each density region becomes the output value while using the image feature group as input data is performed several thousand times or more. Part (# 04).

  In addition, a sample image with a face from which a face area is detected and an appropriate image with a face corresponding to the sample image with the face are extracted from the sample image (# 05). This face area is detected automatically using a known face detection unit. Next, the reference density value of the face area in the sample image with face and the reference density value of the face area in the appropriate image with face are calculated (# 06). As the reference density value, an average density value, an intermediate density value, a mode density value, or the like in the face area can be used, but it has been experimentally confirmed that the average density value is suitable. Further, a regression analysis is performed using a reference density value (average density value) of the face area in the sample image with face as an explanatory variable, and a reference density value (average density value) of the face area in the appropriate image with face as an objective variable, Preferably, a regression line as shown in FIG. 2 is calculated (# 07).

  The input image to be corrected can be corrected to an appropriate image using the neural network unit constructed in this way. The principle of this image correction is schematically shown in FIG. First, an image feature value group (region-independent image feature value) from an image input for correction purposes or a correction calculation image (a type of input image) generated by performing a reduction process to a predetermined size as necessary. Group or region-dependent image feature quantity group or both) is calculated (# 11). The image feature amount group based on the input image obtained in this way is input to the neural network unit (# 12). As a result, a basic correction curve is created from the coefficient defining the correction curve output from the neural network unit (# 13).

  Next, face area detection is performed on the input image (# 14), and a density reference value of the detected face area, for example, an average density value is calculated (# 15). Based on the regression line calculated in advance using the calculated average density value as an explanatory variable value (input density value), the objective variable value (output density value) is obtained as shown in FIG. 4 (# 16). In the example of FIG. 4, the input density value is “156” and the output density value is “160”. The obtained relationship between the input density value and the output density value is used as an adjustment amount for adjusting the basic correction curve. In other words, as exemplified in FIG. 5, the face area of the basic correction curve is emphasized by shifting the basic correction curve so that the output density value “160” is derived with respect to the input density value “156”. Adjustments are made (# 17). The basic correction curve adjusted in this way is set as an execution correction curve (# 18), and image correction of the input image is performed using the execution correction curve (# 19). Further, print data is generated from the corrected image, and the print data is transferred to the printing unit, whereby an image print is output (# 20). If no face area is detected from the input image, the basic correction curve is set as an execution correction curve as it is.

  Image data handled in photographic print processing is a color image represented by R, B, and G density values, which are pixel values of red, blue, and green as main color components. Therefore, the above-described sample image and input image are handled as a color image composed of R, B, and G density values, an image feature group is obtained for each of R, B, and G, and a basic correction curve for each of R, B, and G is obtained. Is generated. Accordingly, the regression line is also calculated for each of R, B, and G. Furthermore, for example, it is also possible to calculate a regression line based on the luminance value obtained by adding each R, B, and G density value and dividing by 3, to obtain an image feature group and generate a basic correction curve It is. In the image correction based on the luminance value, it is preferable to return the corrected luminance value finally obtained to the R / B / G density value again from the R / B / G component ratio. However, in the following description, the color components and luminance will not be described separately, but will be described in a unified manner. Therefore, the phrase density value should be interpreted as including the density value of each color and the luminance value calculated from it.

  Next, an example of a specific construction procedure of the above-described neural network unit will be described with reference to FIGS. FIG. 6 shows a schematic structure of the neural network unit. The neural network unit includes an input layer having a large number of input elements, an intermediate layer having a large number of intermediate elements, and an output layer having a large number of output elements. In general, the number of input elements, intermediate elements, and output elements decreases in that order. When an image feature quantity group of the input image is input to the input element, a predetermined number of coefficients that define the basic correction curve are output as the basic correction curve to the output element. A known approximate curve creation algorithm can be used as an algorithm for creating a smooth correction curve using the output coefficient.

The image feature quantity group used at the time of learning in the neural network section includes area-dependent image feature quantities, that is, feature quantities that depend on image areas, and area-independent image feature quantities, that is, feature quantities that do not depend on image areas. Used.
Examples of region-dependent image feature values include:
Average density value of the images the central portion (see (a) in FIG. 7), the average density of the image peripheral portion average density value of ((a) see FIG. 7), the image upper portion (see (b) in FIG. 7) Value, average density value of the lower side of the image (see FIG. 7B), average density value of the left side of the image (see FIG. 7C), right side of the image (see FIG. 7C) average density value, the maximum density value of the images the central portion (see (a) in FIG. 7), the image periphery maximum density value (in FIG. 7 (a) refer), the image upper portion (FIG. 7 (b), see ), The maximum density value at the lower side of the image (see FIG. 7B), the maximum density value at the left side of the image (see FIG. 7C), and the right side of the image (see FIG. 7C). maximum concentration value of) the reference), the minimum density value of the images the central portion (see (a) in FIG. 7), the image peripheral portion (minimum density value of the see FIG. 7 (a)), the image upper portion (in FIG. 7 Minimum density value (see (b)), below image Density (see FIG. 7B), minimum density value on the left side of the image (see FIG. 7C), minimum density value on the right side of the image (see FIG. 7C), top and bottom The average value of the difference values (see (d) of FIG. 7) of the pixels adjacent to .
Area-independent image feature values include the average density value of the entire image, the maximum density value of the entire image, the minimum density value of the entire image, the density standard deviation of the entire image, the maximum density value of the entire image, and the minimum density value of the entire image. , each gray scale region integrated frequency value obtained by integrating the frequency of the tone values included in each region where the gradation value axis of a histogram 20 divided for each color component (see FIG. 8) are representative.

FIG. 9 shows a routine for constructing a neural network unit having the above input / output configuration and calculating a face area regression line.
First, the prepared sample image is read (# 30), each image feature amount described above is calculated from the sample image (# 31), and these are stored in the memory as an image feature amount group (# 32). Manual image correction by an expert is performed while displaying the read sample image on the monitor (# 33). A printout of the corrected image is performed as necessary (# 34), and it is checked whether the corrected image is appropriate (# 35). Manual correction is repeated until the corrected image is determined to be appropriate, and when an appropriate corrected image is obtained, a correction curve that converts the pixel value of the original image into the pixel value of the appropriate corrected image based on the image correction ( A coefficient defining the correction curve is created and stored in the memory (# 36). Next, it is checked whether a face area is detected from the sample image read in step # 30 (# 37). When the face area is detected (# 37 Yes branch), the average density value of the pixels included in the face area of the sample image and its appropriate corrected image is calculated (# 38) and stored in the memory (# 39). If no face area is detected (# 37 No branch), the processing of steps # 38 and # 39 is jumped.

  The processing from step # 30 to step # 39 is executed for all the sample images prepared (# 40 Yes branch). When the processing for all the sample images is completed (# 37 No branch), the image feature quantity group stored in the memory and the correction curve stored in the memory, more precisely, the coefficient defining the manual correction curve is used as the learning data. Assign to the network part (# 41). The neural network unit learns an image feature group as input data, and a coefficient that defines a manual correction curve as teacher data, determines its input / output relationship (coupling coefficient and threshold), and constructs a neural network unit ( # 42).

  Next, the average density value of the face area of all the sample images in which the face area is detected and all the average density values of the pixels included in the face area of the corresponding proper correction image are read out from the memory (# 43). Regression analysis is performed using the reference density value (average density value) of the face area in the detected sample image as an explanatory variable, and the reference density value (average density value) of the face area in the appropriate image detected in the face area as an objective variable. A straight line is calculated (# 44).

  Next, a photographic printing apparatus that mounts an image correction apparatus using the image correction technique described above with reference to FIGS. 1 and 2 and handles captured images as images will be described. FIG. 10 is an external view showing the photographic printing apparatus. This photographic printing apparatus includes a printing station 1B as a photographic printing section that performs exposure processing and development processing on the photographic paper P, a developed photographic film 2a, and the like. The operation station 1A is configured to process a captured image taken from an image input medium such as a digital camera memory card 2b and generate / transfer print data used in the print station 1B.

  This photographic printing apparatus is also called a digital minilab. The printing station 1B pulls out the roll-shaped printing paper P stored in two printing paper magazines and cuts it into a print size with a sheet cutter. The photographic paper P is exposed on the surface of the photographic paper P by the print exposure unit, and the exposed photographic paper P is sent to a processing tank unit 15 having a plurality of development processing tanks for development processing. . After drying, the photographic paper P, that is, the photographic prints P, sent to the sorter 17 from the transverse feed conveyor 16 at the upper part of the apparatus is collected in a plurality of trays of the sorter 17 in a state of being sorted in order units. The print exposure unit receives R (red), G (green), and B (blue) based on print data from the operation station 1A along the main scanning direction with respect to the printing paper P conveyed in the sub-scanning direction. A line exposure head for irradiating laser beams of the three primary colors is provided.

  At the upper position of the desk-like console of the operation station 1A, there is a film scanner 20 capable of acquiring photographed image data (hereinafter simply referred to as a photographed image) from a photographed image frame of the photographic film 2a with a resolution exceeding 2000 dpi. A media reader 21 that is arranged and acquires captured images from various semiconductor memories or CD-Rs used as an image recording medium 2b mounted on a digital camera or the like functions as the controller 3 of this photographic printing apparatus. Built into a general-purpose computer. The general-purpose personal computer is also connected with a monitor 23 for displaying various information, and a keyboard 24 and a mouse 25 as operation input devices used as an operation input unit used for various settings and adjustments.

  The controller 3 of this photographic printing apparatus uses a CPU as a core member and constructs a functional unit for performing various operations of the photographic printing apparatus by hardware and / or software, as shown in FIG. As described above, the functional unit particularly related to the present invention includes an image input unit 31 that takes a photographed image read by the film scanner 20 or the media reader 21 and performs preprocessing necessary for the next processing, Graphic user interface (hereinafter abbreviated as GUI) that generates a control command from creation of a graphic operation screen including a window, various operation buttons, and the like, and user operation input through such a graphic operation screen (using the keyboard 24, mouse 25, etc.) Sent from the GUI unit 33 and the GUI unit 33 A print management unit 32 that performs image processing on a captured image transferred from the image input unit 31 to the memory 30 in order to generate desired print data based on a control command or an operation command directly input from the keyboard 24 or the like. Video control for generating a video signal for causing the monitor 23 to display graphic data sent from the GUI source 33, a simulated image as a print source image, an expected finished print image, and a pre-judge print operation such as color correction A print data generation unit 36 for generating print data suitable for the print exposure unit 14 installed in the print station 1B based on the processed photographed image having undergone the image processing, C shot images or processed shot images that have been processed Etc. formatter 37 for formatting into a form for writing to -R and the like.

  When the photographic image recording medium is the film 2a, the image input unit 31 separately sends the scan data for the pre-scan mode and the main scan mode to the memory 30, and performs preprocessing according to each purpose. When the captured image recording medium is the memory card 2b, when the captured image includes a thumbnail image (low resolution data), the captured image main data (for use in displaying a list on the monitor 23) ( The thumbnail image is sent to the memory 30 separately from the high-resolution data. If a thumbnail image is not included, a reduced image is created from this data and sent to the memory 30 as a thumbnail image.

  The print management unit 32 includes a print order processing unit 40 that manages the print size, the number of prints, and the like, and an image processing unit 50 that performs various types of image processing on the captured image developed in the memory 30.

  The image processing unit 50 includes a first image correction unit 60 in which a photo retouching program for manually performing various image processing on the captured image developed in the memory 30 and a neural network as described above. A second image correction unit 70 for performing automatic image correction on the input photographed image developed in the memory 30 using an image correction technique; and a face area detected from the input photographed image, and the position of the detected face area, A face detection unit 80 for outputting the size is basically incorporated in the form of a program.

  As shown in FIG. 12, the second image correction unit 70 having a function as an image correction apparatus according to the present invention includes a correction calculation generated from an input photographed image developed in the memory 30 in this embodiment. The image feature amount calculation unit 71 for obtaining the image feature amount composed of the region-independent image feature amount and the region-dependent image feature amount from the photographed image (256 × 384 image), and the procedure described with reference to FIG. The constructed neural network unit 72, a basic correction curve generating unit 73 that generates a basic correction curve based on an output value output by inputting an image feature quantity group to the neural network unit 72, and FIG. A regression equation table 76 that is a table of regression lines calculated by the procedure described above, and the face area position given by the face detection unit 80, The first preprocessing unit 74 that calculates the average density value of the face area of the input photographed image as the input density value based on the face detection information such as the area size, and the first preprocessing unit 74 using the regression equation table 76 An execution correction curve is generated by adjusting the reference correction curve so that the relationship between the input density value and the output density value is established, and the second preprocessing unit 75 that determines the output density value corresponding to the input density value that has been input. A correction curve adjustment unit 77 and an image correction unit 78 that corrects an input captured image using the execution correction curve are provided. The image correction unit 78 includes an image correction execution unit 78a that corrects an input captured image based on the execution correction curve set by the correction curve setting unit 78b.

  The photographic print processing in the photographic printing apparatus configured as described above will be described with reference to the flowchart of FIG. First, a captured image input to perform photographic print output is read (# 50), and an image feature quantity group is calculated from a correction calculation image created from the captured image (# 51). The image feature quantity group is given to the input element of the neural network unit 72a (# 52). A basic correction curve is created based on the output value from the neural network unit 72a (# 53). Next, the face detection unit 80 checks whether or not a face area can be detected from the read captured image (# 54). When a face area is detected (Yes in # 54), face detection information related to the detected face area is given to the first preprocessing unit 74. The first preprocessing unit 74 calculates the average density value of the pixels included in the face area of the captured image being read based on the given face detection information (# 55). The first preprocessing unit 74 accesses the regression equation table 76 using the calculated average density value as the input density value, and obtains an output density value (# 56). The relationship between the input density value and the output density value is the adjustment amount of the basic correction curve. Adjustment to shift the basic correction curve according to the characteristics of the face area is performed in the manner shown in FIG. The adjusted basic correction curve is set as an execution correction curve in the image correction unit 78 (# 58). If no face area is detected in step # 54 (# 54 No branch), the basic correction curve is set as it is as an execution correction curve. Subsequently, the image correction execution unit 76a corrects the captured image using the set execution correction curve (# 59), and the print data generation unit 36 generates print data from the corrected captured image (# 60). ). Print processing is performed by transferring the print data to the print station 1B (# 61), and a photo print is output (# 62).

  In the description of the embodiment described above, a regression line that is linear is adopted as the regression equation, but, of course, a regression curve such as a quadratic equation or a cubic equation may be adopted.

  When adjusting the basic correction curve based on the regression line related to the face area average density value, in the description of FIG. 5, the basic correction curve is adjusted by moving horizontally, but the basic correction curve is adjusted by moving vertically. Also good. Further, a predetermined reference point may be set and the basic correction curve may be rotated for adjustment. The important point in the present invention is that the correction relationship of the average density value of the face area is obtained by a regression line, and the basic correction curve is adjusted based on this relationship.

  Here, an example in which the image correction technology according to the present invention is incorporated in a photo printing apparatus called a minilab installed in a DP shop is taken up, but self-service installed in a store of a convenience store or DP shop. It may be incorporated into various photographic printing apparatuses such as a photographic printing apparatus, or may be incorporated into a program of the image processing software as one of the photographed image correction functions of the image processing software.

  In the above-described embodiment, the print station 1B serving as the image print output unit exposes the photographic image to the photographic paper P by the print exposure unit 14 including the laser type exposure engine, and prints after the exposure. Although the so-called silver salt photographic printing method for developing the paper P was adopted, of course, the printing method of the photographed image corrected by the present invention is not limited to such a method. And various photographic printing methods such as an inkjet printing method in which an image is formed by ejecting ink onto paper and a thermal transfer method using a thermal transfer sheet.

Explanatory drawing explaining the basic principle used with the image correction technique by this invention Correlation diagram showing correlation of average density value of face area between sample image and manually corrected appropriate image, and explanatory diagram showing its regression line Explanatory drawing explaining the basic principle used with the image correction technique by this invention Explanatory drawing explaining how to obtain an output density value corresponding to an input density value using a regression line Explanatory drawing explaining how to adjust the basic density curve Schematic diagram showing the configuration of a neural network Explanatory drawing explaining the image feature amount depending on the image area Explanatory drawing explaining the image feature amount independent of the image area Flow chart showing the construction routine of the neural network External view of photographic printing apparatus adopting image correction technology according to the present invention Functional block diagram explaining the functional elements built in the controller of the photo printing device 1 is a functional block diagram showing the functional configuration of an image correction unit that employs the image correction technology of the present invention. Flow chart showing the procedure of photo print processing

Explanation of symbols

1B: Print station (print section)
31: Image input unit 36: Print data generation unit 70: Second image correction unit (image correction device)
71: Image feature amount calculation unit 72: Neural network unit (statistical learning rule unit)
74: first preprocessing unit 75: second preprocessing unit 76: regression equation table 77: correction curve adjustment unit 78: image correction unit 78a: image correction execution unit 78b: correction curve setting unit 80: face detection unit

Claims (6)

In an image correction method for correcting an input image to a proper image using a correction curve,
A statistical learning rule unit that learns, as an input value, an image feature amount of an image sequentially selected as an original image from a number of prepared sample images as a teacher value and a reference correction curve that creates an appropriate image from the original image Building steps,
Extracting a face sample image in which a face region is detected from the sample image and a face appropriate image corresponding to the face sample image;
Calculating a regression equation from the reference density value of the face area in the sample image with face and the reference density value of the face area in the appropriate image with face;
Obtaining an image feature quantity group from the input image;
Creating a reference correction curve based on an output value output from the statistical learning rule unit by inputting an image feature quantity group of the input image to the statistical learning rule unit;
Calculating a reference density value of the face area detected from the input image as an input density value;
Determining an output density value derived from the input density value using the regression equation;
Generating an execution correction curve by adjusting the reference correction curve so that the relationship between the input density value and the output density value is established;
Image correcting the input image with the execution correction curve;
An image correction method comprising: The image correction method according to claim 1, wherein the statistical learning rule part is a neural network part. The image to be corrected is a color image composed of R, G, and B density values, and the correction curve and the regression equation are calculated from the R density value, G density value, B density value, and R, G, B density value. The image correction method according to claim 1, wherein the image correction method is created for each luminance value to be processed. The image correction method according to claim 1, wherein the reference density value is an average density value of a face area. The image correction method according to claim 1, wherein the regression equation is a regression line. In an image correction apparatus that corrects an input image to a proper image using a correction curve,
A statistical learning rule constructed by learning, as a teacher value, a reference correction curve that uses an image feature amount of an image sequentially selected as an original image from a large number of prepared images as an input value and creates an appropriate image from the original image And
A regression equation table storing a regression equation calculated from the reference density value of the face region in the sample image with face from which the face region extracted from the sample image is detected and the appropriate image with face corresponding to the sample image with face;
An image feature quantity computing unit for obtaining an image feature quantity group from the input image;
A reference correction curve generation unit that creates a reference correction curve based on an output value output from the statistical learning rule unit by inputting an image feature amount group of the input image to the statistical learning rule unit;
A first pre-processing unit that calculates a reference density value of the face area detected from the input image as an input density value;
A second pre-processing unit that determines an output density value derived from the input density value using the regression equation table;
A correction curve adjustment unit that generates an execution correction curve by adjusting the reference correction curve so that the relationship between the input density value and the output density value is established;
An image correction unit that corrects the input image with the execution correction curve;
An image correction apparatus comprising:

Images