JP5062156B2 - Photo image processing method, photo image processing program, and photo image processing apparatus - Google Patents

Description

  The present invention relates to a photographic image processing method, a photographic image processing program, and a photographic image processing apparatus that suppress granular noise included in photographic image data.

  When an image taken with a digital camera is observed, it is observed that fine granular noise is superimposed on the image. In particular, noise that is rough in the dark part of the image is conspicuous, and similar noise appears in the entire image in an image shot in a dark place without flash emission.

  These noises are caused by the characteristics of the elements such as the reset noise and dark current noise of the image sensor.The imaging environment is worse, that is, the lower the illuminance of the subject, the better the noise component. Noise is enhanced when the brightness is corrected.

  Similarly, a film image taken by an optical camera also has grainy irregularities based on the size of the pigment cloud, and fine grained noise is superimposed on the photographic image data converted into a digital image through a film scanner. ing. These granular noises are noticeable when film particles are imaged on a highly sensitive film or when a photographic image is enlarged.

  In order to suppress these granular noises, image processing is performed using a median filter or Gaussian filter having a predetermined filter size, that is, processing for replacing a certain pixel with a predetermined average value obtained by taking the surrounding pixels into consideration. There is processing that is applied to the whole, but if such blurring processing is performed after sharpening processing that sharpens the contour portion of the image, the contour is blurred, so the meaning of sharpening is lost, and the above blurring processing is performed. In the case of performing the sharpening process after being performed, there is a disadvantage that fine structures (details) in the image disappear.

  Therefore, photographic image data is divided into blocks by a predetermined number of pixels, the average value and variance value of each area are obtained, and a flat image area with a small variance value is subjected to strong smoothing processing, and an image including an outline with a large variance value Although it is conceivable to perform weak smoothing processing on the area, according to this method, if an image area including a contour is present in the block, the weak smoothing processing is uniformly executed. The noise suppression effect is reduced in the band-like region of the width, and a preferable result is not obtained.

  In Patent Document 1, as an image processing method for performing weighted average filter processing on photographic image data converted into a digital image via a film scanner, a step of sequentially setting a pixel of interest for each RGB pixel component constituting the photographic image data Calculating a difference value of pixel values between each peripheral pixel located in the calculation region for the weighted average filtering process centered on the target pixel and the target pixel, and weighted average filtering according to the difference value There has been proposed a method including a step of determining a weighting factor used for processing, and a step of executing a weighted average filtering process using the weighting factor to obtain a corrected pixel value of a target pixel.

According to this method, there is a predetermined relationship between the pixel value of the pixel of interest and its surrounding pixels when the pixel of interest is located in the flat region and when it is located in the contour region. By setting a weighting factor that gives a strong influence, granular noise can be suppressed not only in flat areas but also in and around the contour and granular details and textures of photographs can be suppressed. It becomes.
JP 2006-302023 A

  However, when suppressing granular noise, a filter or filter coefficient of an appropriate size based on the characteristics of the original image data, that is, the number of pixels of the original image data, the level of granular noise included in the original image, etc. However, in the conventional technique described above, the degree of the image cannot be grasped, and the granularity suppression processing is performed with a uniform filter coefficient and filter size, so the quality of the obtained image is different. There was a problem.

  For example, if uniform weighted average filter processing is applied to an original image with a relatively low level of granular noise, the flat image will appear as an uninflated image, and the original image will have a relatively high level of granular noise. On the other hand, when the uniform weighted average filter processing is performed, the granular noise cannot be sufficiently suppressed.

  Therefore, the original image is divided into a plurality of pixel blocks of a predetermined size, each pixel block is grouped according to the average density value, and the degree of variation in density value calculated for each group is divided into pixel blocks belonging to each group. Assuming that the fluctuation range of the included granular noise is shown, it is conceivable to perform the noise suppression processing of each pixel block with the noise suppression intensity corresponding to the noise fluctuation range.

  For dark pixel blocks where dark current and gain noise, which is generally measured with the most attention, are prominent, increase the noise suppression strength, and for bright pixel blocks where noise is not conspicuous, use a pattern. It is ideal to reduce the noise suppression strength to such an extent that it is not lost and to perform noise suppression processing. However, for example, in the case of an image in which the pattern is complex and has few flat parts and the pattern is recognized as noise, or an image with an extremely large number of pixel blocks in the dark part or bright part, the above ideal is In contrast, the noise fluctuation range may increase as it becomes brighter, and when noise suppression processing is performed with the noise suppression intensity corresponding to the noise fluctuation range, the noise in the dark part is noticeable and the picture in the bright part is lost. The image quality sometimes deteriorated.

  In addition, when photographic image data taken with a digital camera is usually compressed into a JPEG image, the color difference data is reduced to 1/2 or 1/4 and then orthogonally transformed. In combination with the reduction in the data amount of the color difference data, the color noise tends to appear larger than the luminance noise. In the method of performing weighted average filtering for each color component described in Patent Document 1 described above, the luminance noise There was also a problem that color noise could not be removed properly.

  The present invention provides a photographic image processing method, a photographic image processing program, and a photographic image processing apparatus capable of specifying an appropriate noise fluctuation range from photographic image data and effectively suppressing noise in view of the above-described conventional drawbacks. In the point.

  In order to achieve the above-mentioned object, the first characteristic configuration of the photographic image processing method according to the present invention is the fluctuation range of the granular noise included in the photographic image data as described in claim 1 of the claims. A photographic image processing method for performing noise suppression processing based on a density component extraction step for extracting density component image data from the photographic image data, and the density component image data extracted in the density component extraction step as predetermined A block characteristic value calculating step for calculating a block characteristic value including an average density value, a maximum density value, a minimum density value, and a density variation for each pixel block by dividing the pixel block into size pixel blocks; In a two-dimensional coordinate system including a Y axis indicating a block characteristic value, the density range is divided into a plurality of sections at predetermined intervals, and each pixel is determined based on the average density value. A pixel block allocating step for allocating locks to corresponding sections, and a pixel block extracting step for extracting, from the pixel blocks allocated to each section, a pixel block having the smallest density variation as a representative pixel block, Calculates the maximum regression line using each average density value of multiple representative pixel blocks extracted in the extraction step as an explanatory variable and each maximum density value as a dependent variable and the minimum regression line using each minimum density value as a dependent variable. A noise fluctuation range specifying step for specifying a range defined by the maximum regression line and the minimum regression line as a fluctuation range of the granular noise, and the minimum regression calculated in the noise fluctuation range specification step Based on the straight line, the maximum regression line is corrected, and the corrected maximum regression line and minimum regression line are corrected. In that it includes a noise variation width correction step of identifying a range defined as the fluctuation width of the granular noise.

  In the block characteristic value calculation step, the density component image data extracted in the density component extraction step is divided into pixel blocks of a predetermined size, and each pixel block includes an average density value, a maximum density value, a minimum density value, and a density variation. A characteristic value is calculated. When the pixel block belongs to the flat part of the image, there is not much difference between the maximum density value and the minimum density value, and the density variation is small, but when the pixel block belongs to the contour part of the image, the maximum density value and the minimum density value There is a large difference in density, and the density variation is relatively large. That is, it can be said that the influence of the variation due to the granular noise is remarkably expressed in the pixel block having a small density variation.

  In the pixel block allocation step, each pixel is divided into any one of the density ranges divided into a plurality of sections at a predetermined interval in a two-dimensional coordinate system including an X axis indicating the density range and a Y axis indicating the block characteristic value. Blocks are allocated based on their average density value. In the pixel block extraction step, a pixel block having a minimum density variation included in the block characteristic value is extracted as a representative pixel block for each divided density range. That is, the density variation of the representative pixel block indicates the noise fluctuation range of the pixel block including the flattest region in the divided density range.

  In the noise fluctuation range specifying step, regression calculation is performed using the noise fluctuation range for each divided density range extracted in the pixel block extraction step, so that the original density range is used instead of the divided density range. Continuous noise fluctuation range can be calculated. As a result of actual verification by the inventors, it has been found that the noise fluctuation width obtained by the present invention substantially matches the fluctuation width of the granular noise.

  However, the inventor of the present application, for example, changes in noise indicated by two regression lines calculated for an image with a complicated pattern, an image with low under-contrast, an image in which a pixel block having a sufficient density range cannot be collected, etc. It has been found that the width becomes wider as it becomes brighter, or that the Y-axis intercept of the maximum regression line may be smaller than the Y-axis intercept of the minimum regression line.

  In other words, a lot of noise related to dark current and gain, which is generally measured with the most attention, is recognized, and the bright part noise that is not noticeable is moderately suppressed so that pixels in the bright part are not mistakenly recognized as noise. It has been found that noise fluctuation ranges that are not ideal but can be obtained are ideal.

  In this way, even in the case where a noise fluctuation range that is not ideal due to the image content such as a pattern is calculated in the noise fluctuation width specifying step, the noise fluctuation width correction step results in a minimum-side regression line. Since the maximum regression line is corrected based on this, the noise fluctuation range can be appropriately corrected.

  In the second feature configuration, as described in claim 2, in addition to the first feature configuration described above, the noise fluctuation range correction step may be configured such that the slope of the maximum regression line is the slope of the minimum regression line. And the range defined by the maximum-side regression line and the minimum-side regression line after the first correction is specified as the fluctuation range of the granular noise.

  According to the above-described configuration, in the noise fluctuation range correction step, correction is performed so that the slope of the maximum regression line matches the slope of the minimum regression line, so that the noise fluctuation range of the bright part is narrowed. Therefore, it is possible to suppress the image pattern from being recognized as noise in a bright part where noise is not noticeable.

  In the third feature configuration, as described in claim 3, in addition to the second feature configuration described above, the noise fluctuation range correction step includes a Y axis of the maximum regression line after the first correction. When the intercept is smaller than the Y-axis intercept of the minimum regression line, the maximum regression line after the first correction is based on the ratio of the Y-axis intercept of the maximum regression line before and after the first correction. The second correction for correcting the Y-axis intercept is performed, and a range defined by the maximum-side regression line and the minimum-side regression line after the second correction is specified as the fluctuation range of the granular noise. .

  In the noise fluctuation range correction step, by performing the first correction, the maximum regression line after the first correction becomes lower in the two-dimensional coordinate system than the minimum regression line, and the noise fluctuation range is a negative value. May cause inconvenience. However, according to the configuration described above, in the noise fluctuation range correction step, the Y-axis intercept of the maximum-side regression line after the first correction is corrected by the second correction, so that the inconvenience can be avoided.

  In the fourth feature configuration, in addition to any one of the first to third feature configurations described above, the noise fluctuation range correction step includes a correlation between the maximum-side regression lines. When the number exceeds a predetermined threshold, the maximum regression line is corrected.

  For example, when calculating the noise fluctuation range of an image whose picture is significantly biased in the dark part and the bright part, the number of pixel blocks assigned to a specific density range by the pixel block assignment step increases. However, it is difficult to say that the amount of noise is appropriately calculated in a density range where the number of allocated pixel blocks is small. That is, when the correlation coefficient of the regression line calculated in the noise fluctuation range specifying step is low, it is difficult to say that the calculated noise fluctuation range is appropriate.

  According to the above configuration, in the noise fluctuation range correction step, when the correlation coefficient of the maximum regression line exceeds a predetermined threshold value, the maximum regression line is corrected and the noise fluctuation range is specified. It is possible to avoid calculating a noise fluctuation range that is difficult to say.

  In the fifth feature configuration, as described in claim 5, in addition to any of the first to fourth feature configurations described above, the noise fluctuation range correction step includes a slope of the maximum regression line. When larger than the minimum regression line, the predetermined size of the pixel block is reduced, and based on the pixel block of the predetermined size after the reduction, the maximum fluctuation regression line is determined through the noise fluctuation width specifying step. And recalculating the minimum regression line and correcting the recalculated maximum regression line based on the recalculated minimum regression line.

  According to the above configuration, in the noise fluctuation range correction step, when the slope of the maximum regression line is larger than the minimum regression line, the predetermined size of the pixel block is reduced to a reduced size pixel block. Based on the noise fluctuation range specifying step, the maximum regression line and the minimum regression line are recalculated. Therefore, compared with the initially calculated regression line, the recalculated regression line is calculated based on a larger number of pixel blocks. Therefore, the noise fluctuation in which the noise amount of each divided density range is appropriately determined You can get the width.

  In addition, in the pixel block of the initial size, when both the pixel indicating the outline portion and the pixel indicating the background portion are included in the pixel block, the density difference between the two pixels indicating the pattern is calculated as noise. However, by reducing the size of the pixel block, it is possible to divide into pixel blocks that include only the pixels in the contour and background portions. Calculation as noise can be avoided.

  In the sixth feature configuration, as described in claim 6, in addition to the fifth feature configuration described above, the density variation is a pixel whose density is higher than the threshold value, with an average density value of each pixel block as a threshold value. This is a value calculated by calculating the difference between the high density side average density value which is the average density value of the pixel and the low density side average density value which is the average density value of the pixels having a density lower than the threshold value.

  According to the above-described configuration, the calculated density variation of each pixel block is not simply the difference between the maximum density value and the minimum density value in each pixel block, and thus shows a high density value that exists only slightly in each pixel block. The influence of a pixel or a pixel showing a low density value is reduced, and the density variation degree in the pixel block can be calculated appropriately.

  The seventh feature configuration is based on each regression line obtained in the noise fluctuation range correction step in addition to any one of the first to sixth feature configurations described above. Obtaining a noise fluctuation range for an arbitrary pixel of interest, calculating a noise suppression intensity from the noise fluctuation range, a density difference value between the target pixel and surrounding pixels, and the noise suppression intensity calculation step. A density-corresponding filter coefficient generation step for dynamically calculating a filter coefficient of a weighted average filter having a size corresponding to the density component image data based on the obtained noise suppression strength, and the density-corresponding filter coefficient generation step A density-corresponding noise suppression processing step for performing weighted average filtering on the density component image data based on a filter coefficient corresponding to the target pixel is further performed. In that it includes certain.

  According to the above configuration, in the noise suppression intensity calculation step, the noise fluctuation range for any target pixel is calculated based on the noise fluctuation range calculated from the flat image area obtained in the noise fluctuation range correction step. Since the noise suppression strength is calculated from the noise fluctuation range, it is necessary to properly express the image contents in the contour image area where the density difference between the target pixel and its surrounding pixels is large, or in the image area where the pattern is fine It becomes possible to calculate the noise suppression strength that is restricted so that the pixel is not recognized as noise.

  In addition, in the density-corresponding noise suppression processing step, the weighted average filter processing is executed using the weighted average filter obtained through the noise suppression strength calculation step and the density-corresponding filter coefficient generation step. By executing the weighted average filtering process that has been exceeded, it is possible to reduce the problem of suppressing and erasing the pattern and outline included in the original image as noise.

  In the eighth feature configuration, as described in claim 8, in addition to the seventh feature configuration described above, the density difference value between the target pixel and the surrounding pixels and the noise obtained in the noise suppression intensity calculation step Based on the suppression strength, a color difference corresponding filter coefficient generation step for dynamically calculating a filter coefficient of a weighted average filter of a size corresponding to the color difference component image data, and each pixel of interest obtained in the color difference corresponding filter coefficient generation step The method further includes a color difference corresponding noise suppression processing step of performing weighted average filtering on the color difference component image data based on the corresponding filter coefficient.

  The inventor of the present application is close to the increase / decrease relationship between the noise of the luminance component of the photographic image data and the noise of the color difference component, and the human eye is sensitive to fine luminance differences, whereas the hue and saturation He knows that he is relatively unaware of small differences.

  According to the above-described configuration, even when the photographic image data is a picture with a fine color change, the weighted average filter process is performed with the noise suppression strength according to the fine density value change of the picture. Therefore, based on the characteristics of the human eye that are less sensitive to changes in hue and saturation than changes in luminance, the color change seen when weighted average filtering is performed on the color difference component with a fixed noise suppression strength. This makes it possible to avoid the problem that a smoothed image that is assimilated with the surroundings is generated.

  The ninth feature configuration is a feature configuration of a photographic image processing program according to the present invention, and performs noise suppression processing based on the fluctuation range of granular noise included in photographic image data as described in claim 9. A photographic image processing program comprising: a density component extraction step for extracting density component image data from the photographic image data; and the density component image data extracted in the density component extraction step is divided into pixel blocks of a predetermined size. A block characteristic value calculating step for calculating a block characteristic value including an average density value, a maximum density value, a minimum density value, and density variation for each pixel block; and dividing the density range into a plurality of sections at predetermined intervals; A pixel block allocation step for allocating each pixel block to the corresponding segment based on the value, and whether the pixel block is allocated to each segment A pixel block extraction step for extracting the pixel block having the smallest density variation as a representative pixel block, and using each average density value of the plurality of representative pixel blocks extracted in the pixel block extraction step as an explanatory variable, A maximum regression line having a maximum concentration value as a dependent variable and a minimum regression line having each minimum concentration value as a dependent variable are calculated, and a range defined by the maximum regression line and the minimum regression line is defined as the granularity. A noise fluctuation range specifying step that is specified as a noise fluctuation range, and the maximum regression line is corrected based on the minimum regression line calculated in the noise fluctuation range specification step, and the corrected maximum side regression A noise fluctuation range correction step for specifying a range defined by a straight line and the minimum regression line as the granular noise fluctuation range; There is a point to be executed.

  In the tenth feature configuration, as described in claim 10, in addition to the ninth feature configuration described above, the noise for any target pixel is based on each regression line obtained in the noise fluctuation range correction step. Based on the noise suppression strength calculation step of obtaining the fluctuation range and calculating the noise suppression strength from the noise fluctuation range, the density difference value between the target pixel and the surrounding pixels, and the noise suppression strength obtained in the noise suppression strength calculation step A density-corresponding filter coefficient generation step for dynamically calculating a filter coefficient of a weighted average filter having a size corresponding to the density component image data, and a filter coefficient corresponding to each pixel of interest obtained in the density-corresponding filter coefficient generation step The image processing apparatus further includes a density-corresponding noise suppression processing step for performing weighted average filtering on the density component image data based on the above.

  The eleventh characteristic configuration is obtained in the step of calculating the noise suppression intensity and the density difference value between the target pixel and the surrounding pixels, in addition to the tenth characteristic configuration described above. Based on the noise suppression strength, a color difference corresponding filter coefficient generation step for dynamically calculating a filter coefficient of a weighted average filter having a size corresponding to the color difference component image data, and each pixel of interest obtained in the color difference corresponding filter coefficient generation step The method further includes a color difference-corresponding noise suppression processing step of performing weighted average filtering on the color difference component image data based on the filter coefficient corresponding to.

  The twelfth characteristic configuration is the characteristic configuration of the photographic image processing apparatus according to the present invention. As described in claim 12, the photographic image having any one of the ninth to eleventh characteristic configurations described above. A photographic image processing apparatus in which a processing program is installed, wherein the photographic image processing apparatus performs noise suppression processing based on a fluctuation range of granular noise included in photographic image data by executing the photographic image processing program In the point.

  As described above, according to the present invention, there is provided a photographic image processing method, a photographic image processing program, and a photographic image processing apparatus that can specify an appropriate noise fluctuation range from photographic image data and can effectively suppress noise. I was able to do it.

  Embodiments of a photographic image processing method, a photographic image processing program, and a photographic image processing apparatus according to the present invention will be described below as a first embodiment.

  As shown in FIG. 1, a photographic image processing apparatus 1 performs an exposure process based on output image data on a photographic paper P, develops the exposed photographic paper, and generates and outputs a photographic print. And an operation station 3 for setting and inputting print order information for the photographic image, performing various image correction processes, and outputting output image data edited from the original image to the photographic printer 2.

  The operation station 3 includes a film scanner 31 that reads an image from a developed photographic film F, and a media driver that reads image data from an image data storage medium M such as a memory card in which image data shot by a digital still camera or the like is stored. 32, a general-purpose computer as the controller 33, and the like.

  As shown in FIGS. 1 and 2, the photographic printer 2 has two systems of photographic paper magazines 21 containing roll-shaped photographic paper P, and the photographic paper P drawn from the photographic paper magazine 21 in a predetermined print size. A sheet cutter 22 for cutting, a back print unit 23 for printing print information such as a frame number on the back of the cut photographic paper P, an exposure unit 24 for exposing the photographic paper P based on the print data, and a post-exposure unit 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 photographic paper P is disposed along the conveyance path of the photographic paper P, and development processing is performed. A lateral feed conveyor 26 that discharges the photographic paper P that has been dried later, and a sorter 27 that sorts a plurality of photographic papers (photo prints) P stacked on the lateral feed conveyor 26 in order units. To have.

  The exposure unit 24 outputs a three-color laser beam bundle of RGB that is 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. Then, an exposure head 24a for exposure is accommodated.

  A transport mechanism 28 composed of a plurality of roller pairs that transport the printing paper P at a predetermined process speed is disposed in the exposure unit 24 and the development processing unit 25 disposed along the transport path. A chucker-type transport mechanism 28a capable of transporting the paper P in multiple rows is provided.

  A controller 33 provided in the operation station 3 operates under the management of a general-purpose operating system, and is installed with a plurality of application programs for executing various image processing and input / output control of the photo processing apparatus 1. As an operation interface, a monitor 34, a keyboard 35, a mouse 36, and the like are connected. The application program includes an image processing program according to the present invention.

  The controller 33 is a block in which the hardware and software cooperate to execute a photo processing process, and will be described below in each functional block.

  As shown in FIG. 3, the controller 33 receives photographic image data as an original image read by the film scanner 31 or the media driver 32, performs a predetermined preprocessing, and transfers it to the memory 41. A print operation information and image editing information is displayed on the screen of the monitor 34, and a graphic operation screen for displaying operation icons for inputting necessary data is generated for the print operation information or a keyboard for the displayed graphic operation screen. 35, a graphic user interface unit 42 that generates various control commands based on input operations from the mouse 36, photographic image data transferred from the image input unit 40, photographic image data after correction processing by the image processing unit 47, Correction parameters at that time, and also set A memory 41 in which lint order information is partitioned and stored in a predetermined area, an order processing unit 43 that generates print order information, and each frame image or a predetermined number of frames for each photographic image data stored in the memory 41 An image processing unit 47 that performs density correction processing, contrast correction processing, and the like on the image is provided.

  Further, a display control unit 46 including a video RAM for displaying the image data developed in the memory 41 based on a display command from the graphic user interface unit 42 and various input / output graphic data on the monitor 34, and the like; A print data generation unit 44 that generates print data for outputting the final corrected image for which various correction processes have been completed to the photographic printer 2, and a storage medium such as a CD-R for the final corrected image according to the customer's order A formatter unit 45 for converting to a file format for writing to the file.

  The film scanner 31 is configured to operate in two modes: a pre-scan mode for reading an image recorded on the film F at a high speed although it is a low resolution and a main scan mode for reading at a high resolution although it is a low resolution. Various correction processes are performed on the low-resolution image read in the mode, and the final correction for the high-resolution image read in the main scan mode based on the correction parameters stored in the memory 41 at that time. The 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 and stored in the memory 41 at that time. Based on the correction parameters, the final correction processing for the high-resolution captured image is executed. When the thumbnail image is not included in the image file, the thumbnail image is generated from the high-resolution captured image by the image input unit 40 and transferred to the memory 41.

  As described above, various editing processes that are frequently trial and error are performed on low-resolution images, so that the calculation load of the controller 33 is reduced.

  The image processing unit 47 includes a distortion correction unit 50 that corrects distortion caused by the photographic lens with respect to photographic image data that is an original image stored in the memory 41, and a granular noise suppression processing unit 51 that suppresses granular noise. A sharpening processing unit 52 that enhances an edge of an image and suppresses noise, a color correction unit 53 that adjusts a color balance so as to reproduce a natural color, and an image size suitable for the size of a photographic print A plurality of image processing blocks such as the enlargement / reduction processing unit 54 are provided.

  The granular noise suppression processing unit 51 functions as a granular noise suppression processing unit 51 for film images that suppresses granular noise included in image data read from the photographic film F in the main scan mode, and an image processing apparatus according to the present invention. In addition, a granular noise suppression processing unit 51 for a digital camera photographed image that suppresses granular noise included in high-resolution image data read from the media driver 32 is provided.

  Hereinafter, processing of photographic image data taken by a digital camera input from the media driver 32 will be described in detail. However, photographic image data read from the photographic film F in the main scan mode can be processed in the same manner. is there.

  When a plurality of thumbnail image data and high resolution image data read from the media driver 32 are stored in the memory 41, several frames of photographic images based on the thumbnail image data and a graphic operation screen are displayed on the screen of the monitor 34. . Normally, since the digital image stored in the medium is compressed by the JPEG method, image data obtained by inverse conversion and decompression is stored.

  For each frame image displayed on the screen of the monitor 34, the order processing unit 43 sets the print conditions, that is, the number of prints, the print size, and the like through the operator's operation on the graphic operation screen.

  Further, distortion correction, sharpening processing, and color correction are executed for each frame image by the image processing unit 47 through an operator's operation, and the image correction processing conditions set at this time are stored in the memory 41. .

  When the print condition setting and correction processing operations for each frame image displayed on the screen of the monitor 34 are completed, the screen is scrolled to the next screen, and the same processing is executed for all the frame images.

  When all the operation processes are completed and a print output operation is performed via the graphic operation screen, distortion correction, granular noise suppression process, and sharpening process are performed on the high-resolution photographic image data stored in the memory 41. Image processing such as color correction processing and enlargement / reduction processing is executed in order, and the image data after the image processing is converted and generated into print data by the print data generation unit 44 and output to the photographic printer 2.

  Distortion correction, sharpening processing, and color correction processing for high-resolution photographic image data are automatically corrected based on correction processing conditions set for thumbnail images, that is, correction processing conditions stored in the memory 41. Processing is executed.

  Hereinafter, the granular noise suppression processing unit 51 for a digital camera photographed image according to the present invention will be described in detail.

  As shown in FIG. 4, the granular noise suppression processing unit 51 searches a plurality of flat image areas from the density component image data constituting the photographic image data, uses the average density value of each flat image area as an explanatory variable, and sets each maximum The maximum regression line with the density value as the dependent variable and the minimum regression line with each minimum density value as the dependent variable are calculated, and the range divided by the maximum regression line and the minimum regression line is included in the photographic image data Based on the noise fluctuation range specifying unit 70 specified as the fluctuation range of the granular noise and the minimum side regression line calculated by the noise fluctuation range specifying unit 70, the maximum side regression line is corrected, and the corrected maximum side regression is performed. A noise fluctuation range correction unit 77 that specifies a range divided by a straight line and a minimum-side regression line as a fluctuation range of granular noise, and an arbitrary target pixel based on each regression line obtained by the noise fluctuation range correction unit 77 Vs. A noise suppression intensity calculation unit 71 that calculates a noise fluctuation range and calculates a noise suppression intensity from the noise fluctuation range, a density difference value between the target pixel and surrounding pixels, and a noise suppression obtained by the noise suppression intensity calculation unit 71 Based on the intensity, a density-corresponding filter coefficient generation unit 72 that dynamically calculates a filter coefficient of a weighted average filter having a size corresponding to the density component image data, and each target pixel obtained by the density-corresponding filter coefficient generation unit 72 A density-corresponding noise suppression processing unit 73 that performs weighted average filter processing of the density component image data based on the corresponding filter coefficient.

  Further, the granular noise suppression processing unit 51 is a weighted average filter having a size corresponding to the color difference component image data based on the density difference value between the target pixel and the surrounding pixels and the noise suppression strength obtained by the noise suppression strength calculation unit 71. A color difference correspondence filter coefficient generation unit 75 that dynamically calculates the filter coefficient of the color difference, and weighted average filter processing of the color difference component image data based on the filter coefficient corresponding to each target pixel obtained by the color difference correspondence filter coefficient generation unit 75 The color difference corresponding noise suppression processing unit 76 is provided.

  Further, the granular noise suppression processing unit 51 includes noise-suppressed luminance component image data obtained from a density-corresponding noise suppression processing unit 73 described later and a noise-suppressed color difference component image obtained from the color-difference noise suppression processing unit 76. An RGB conversion processing unit 74 that converts each YCbCr component of each pixel obtained from the data into an RGB component is provided.

  The noise fluctuation width specifying unit 70 divides the density component image data extracted by the density component extraction unit 701 and the density component image data extracted by the density component extraction unit 701 into pixel blocks of a predetermined size, A block characteristic value calculation unit 702 that calculates a block characteristic value including an average density value, a maximum density value, a minimum density value, and density variation for each pixel block, and an X axis indicating a density range and a Y axis indicating a block characteristic value. In a two-dimensional coordinate system, a density range is divided into a plurality of sections at predetermined intervals, and a pixel block assigning unit 703 that assigns each pixel block to a corresponding section based on an average density value, and a pixel block assigned to each section A pixel block extraction unit 704 that extracts a pixel block having a minimum density variation as a representative pixel block; and a pixel block extraction unit 70 Regression to calculate the maximum regression line using each average density value of multiple representative pixel blocks extracted in step 1 as explanatory variables and each maximum density value as a dependent variable and minimum regression line using each minimum density value as a dependent variable A straight line calculation unit 705 is provided.

More specifically, the density component extraction unit 701, based on the following conversion formula, reads photographic image data that is a high-resolution original image read from the memory 41 (for example, if it is 8 million pixels, 3264 pixels wide × 2448 vertical). Each of R (red), G (green), and B (blue) color components (hereinafter referred to as “RGB”) of each pixel that constitutes a pixel is converted into a luminance color difference component and stored in memory. 41.
Y = 0.33333R + 0.33334G + 0.33333B
Cb = −0.16874R−0.33126G + 0.50000B
Cr = 0.50000R-0.41869G-0.0811B

  As shown in FIG. 5A, the block characteristic value calculation unit 702 divides the luminance component image data extracted by the density component extraction unit 701 into pixel blocks having a predetermined size as density component image data. The average density value, maximum density value, and minimum density value are calculated. Subsequently, as shown in FIG. 5B, using the average density value as a threshold value, the high density side average density value, which is the average density value of the pixels H having a higher density than the threshold value, and the pixel L having a lower density than the threshold value. A low density side average density value, which is an average density value, is calculated, and further, a difference between the high density side average density value and the low density side average density value is calculated as indicating density variation of the pixel block.

  As described above, the block characteristic value calculation unit 702 calculates the block characteristic value including the average density value, the maximum density value, the minimum density value, and the density variation for each pixel block of a predetermined size, and the first row of the processing target image data. Each pixel block arranged in the row direction from the leftmost pixel block is repeatedly processed to calculate the block characteristic value as a pixel block of interest, and when the processing is completed up to the rightmost pixel block, the processing is completed from the leftmost pixel block of the second row. Similar processing is performed in the row direction, and the processing is repeated up to the rightmost pixel block in the last row.

  The pixel block allocating unit 703 divides the density range into a plurality of sections at a predetermined interval in a two-dimensional coordinate system including the X axis indicating the density range and the Y axis indicating the block characteristic value, and is calculated by the block characteristic value calculating unit 702. Each pixel block is assigned to a corresponding category based on the average density value. For example, in FIG. 5C, the density range of 256 gradations is divided into 16 sections by the pixel block allocating unit 703, the X axis shows the divided density range, and the Y axis shows density variation. The result of assigning each pixel block is shown.

  The pixel block extraction unit 704 extracts a pixel block having the smallest density variation as a representative pixel block from the pixel blocks allocated to each section by the pixel block allocation unit 703. For example, as shown in FIG. 5C, even in the case of image data in which pixel blocks are concentrated and allocated in two density ranges, a high density range and a low density range, A pixel block having a minimum density variation is extracted in each of the low density ranges.

  That is, the pixel block of the flattest region with the least density variation within each of the density ranges divided at predetermined intervals is extracted as a representative pixel block for noise suppression processing.

  For example, as shown in FIG. 5D, the regression line calculation unit 705 uses each average density value of a plurality of representative pixel blocks extracted by the pixel block extraction unit 704 as an explanatory variable (X axis), and sets each maximum density. The two regression lines (slope a, intercept b, correlation coefficient r) with each value and each minimum concentration value as dependent variables (Y-axis) are calculated by the method of least squares, etc. Is specified as the fluctuation range of the granular noise.

  However, the inventor of the present application, for example, when calculating the above two regression lines for an image with a complicated pattern, an image with low under-contrast, an image in which a pixel block having a sufficient density range cannot be collected, etc. As shown in FIG. 6 (a), the fluctuation range of the noise becomes wider as the bright part becomes, or as shown in FIG. 6 (b), the Y-axis intercept of the maximum regression line is the minimum regression line. Has been found to be smaller than the Y-axis intercept.

  In other words, a lot of noise related to dark current and gain, which is generally measured with the most attention, is recognized, and the bright part noise that is not noticeable is moderately suppressed so that pixels in the bright part are not mistakenly recognized as noise. It has been found that noise fluctuation ranges that are not ideal but can be obtained are ideal. Therefore, the noise fluctuation range correction unit 77 corrects the slope of the regression line and the Y-axis intercept as follows.

  For example, as shown in FIG. 6A, when the slope of the maximum regression line is larger than the slope of the minimum regression line, the slope a of the maximum regression line is matched with the slope of the minimum regression line. Perform the first correction. That is, in this case, the noise fluctuation range of the bright part is narrowed, and it is possible to suppress the image pattern from being recognized as noise in the bright part where the noise is not noticeable.

  However, for example, as shown in FIG. 6B, when the Y-axis intercept of the maximum regression line is smaller than the Y-axis intercept of the minimum regression line, even when the first correction is performed, The maximum correction regression line after the first correction is lower in the two-dimensional coordinate system than the minimum regression line, and there may be a problem that the noise fluctuation range shows a negative value. Therefore, when the Y-axis intercept of the maximum-side regression line after the first correction is smaller than the Y-axis intercept of the minimum-side regression line, the noise fluctuation range correction unit 77 calculates the maximum-side regression line before and after the first correction. Based on the ratio of the Y-axis intercept, the second correction for correcting the Y-axis intercept of the maximum-side regression line after the first correction is performed.

More specifically, first, the first corrected Y-axis intercept maxB ′ is calculated based on the first corrected maximum regression line according to the following equation.
aveY = maxA ′ × aveX + maxB ′
⇒ maxB '= aveY-maxA' x aveX

  Here, aveX and aveY are the average density values in the plurality of representative pixel blocks, which are explanatory variables, and the plurality of dependent variables, respectively, when the maximum fluctuation line is calculated by the noise fluctuation range specifying unit 70. Each maximum density value in the representative pixel block is shown. Further, maxA ′ represents the slope of the maximum regression line after the first correction.

  Further, the inventor of the present application has found that when the above two regression lines are calculated, the correlation coefficient r is within 0.99 in most images regardless of the content of the image to be calculated. Yes. In addition, the inventor of the present application, for example, when calculating the above two regression lines for an image with a complicated pattern, an image with low under-contrast, an image in which a pixel block having a sufficient density range cannot be collected, etc. A sufficient correlation coefficient r cannot be obtained, and the relationship between the average density value and the maximum density value and the minimum density value of the pixel block in the flat area indicated by the two calculated regression lines is sufficiently high in linearity. I know it is not. Therefore, the following correction may be performed according to the value of the correlation coefficient of the regression line.

  When the correlation coefficient r is greater than or equal to a first threshold (for example, 0.97), the correction is not performed because a sufficient correlation coefficient r is obtained. When the correlation coefficient is greater than or equal to a second threshold (eg, 0.95) and less than the first threshold (eg, 0.97), the first corrected Y-axis intercept maxB ′ is brought close to zero. Correct as follows. When the correlation coefficient r is less than the second threshold, the calculated regression line is regarded as unreliable, and noise suppression processing using the noise fluctuation range specified based on the regression line described later is not performed. .

More specifically, the degree of approach of the intercept b to 0 is such that when the correlation coefficient r is the second threshold, the first corrected Y-axis intercept maxB ′ is 0, and the correlation coefficient r is the first According to the value of the correlation coefficient r with respect to the difference value (for example, 0.02) between the first threshold value and the second threshold value so that the Y axis intercept maxB ′ after the first correction does not move in the case of the threshold value. That is, it is determined by the following equation.
After correction maxB ′ = maxB ′ × (r−second threshold) / (first threshold−second threshold)

  When the first corrected Y-axis intercept maxB ′ is calculated based on the first corrected maximum regression line, the Y-axis intercept maxB of the original maximum regression line and the first corrected Considering the case where the difference between the Y-axis intercept maxB ′ is greatly different, the ratio between the Y-axis intercept maxB and the first corrected Y-axis intercept maxB ′, that is, the maximum regression line Y before and after the first correction A second correction for correcting the Y axis intercept maxB ′ after the first correction is performed according to the ratio of the axis intercept.

More specifically, the fusion ratio rto is calculated according to the ratio of the Y-axis intercept of the maximum regression line before and after the first correction according to the following formula, and the Y-axis intercept maxB and the The fusion processing of the Y axis intercept maxB ′ after the first correction is performed, and the Y axis intercept maxB ″ by the second correction is calculated.
rto = MAX (MIN (maxB / maxB′−C1) / C2,1), 0)
maxB ″ = maxB × rto + maxB ″ × (1−rto)

  Here, C1 and C2 indicate variables for adjusting the fusion ratio rto. As a result of verifying a plurality of photographic image data, the inventor of the present application has found that, for example, an appropriate noise fluctuation range is required when C1 is 0.15 and C2 is 0.3.

  When the noise fluctuation range correction unit 77 calculates the Y-axis intercept maxB ″ by the second correction, the Y-axis intercept of the maximum regression line after the first correction is the Y-axis intercept maxB ″ by the second correction. Thus, the maximum correction regression line after the first correction is translated, and the second correction ends.

  When the Y-axis intercept maxB '' by the second correction is smaller than the Y-axis intercept of the minimum regression line, the calculated regression line is regarded as unreliable and is based on the regression line described later. Noise suppression processing using the specified noise fluctuation range is not performed.

  Further, as shown in FIG. 6C, the two regression lines are assumed to have a negative slope and intersect the minimum regression line, as shown in FIG. 6C. . In this case, since the ideal noise fluctuation range described above can be considered in the darker density range than where the two regression lines intersect, the regression line described later is not corrected without correcting the regression line. It is assumed that noise suppression processing using the noise fluctuation range specified based on the above is performed. On the other hand, the noise suppression process using the noise fluctuation range specified based on the regression line described later is not performed in the density range in the brighter part than the location where the regression lines intersect.

  In this way, the variation width of the granular noise with respect to the density of each pixel is determined from the pixel block in the flattest area where the density variation is small within each density range divided at a predetermined interval constituting the original image data. Be grasped.

  The noise suppression intensity calculation unit 71 includes two regression lines calculated from the flat image region obtained by the noise fluctuation range correction unit 77 (hereinafter, an expression indicating the maximum regression line is referred to as an “upper limit expression”, and a minimum regression line). Is expressed as a “lower limit expression.”), The noise fluctuation range for any target pixel of the density component image data is calculated. That is, the X-axis value of the above-mentioned two regression lines is the average density value of the representative pixel block that is the explanatory variable of the two regression lines, but the X-axis value is the density value of the target pixel. Therefore, the fluctuation range of the noise in the flat image area indicated by the regression line is calculated as indicating the fluctuation range of the noise with respect to the density value of the target pixel.

In the following description, unless otherwise specified, the slope of the upper limit expression is YmaxA, the intercept is YmaxB, the slope of the lower limit expression YminA, and the intercept is YminB. When the density value indicated by the coordinate (x, y) of the target pixel is datx, y, the upper limit value MaxYx, y and the lower limit value MinYx, y of the noise fluctuation range with respect to the density value of the target pixel are expressed by the following equations. Can do.

Therefore, the noise fluctuation range Wx, y with respect to the density value of the target pixel can be expressed by the following equation as a difference value between the upper limit value MaxYx, y and the lower limit value MinYx, y.

  Here, since the calculated noise fluctuation width Wx, y indicates the magnitude of the noise, it can be used as an intensity setting index for noise suppression.

  For example, as shown in FIG. 7A, the distribution of density values in a flat image area such as a background image of a predetermined size, and as shown in FIG. 7B, the flat image area shown in FIG. When the density value distribution in the image region of a predetermined size including the contour image region including the contour portion of the subject is normalized and compared, in the image region including the flat image region and the contour image region, the flat image region and the contour image It can be seen that the standard deviation σ in the entire image area is larger than the value of the standard deviation σ when each of the areas is normalized. That is, depending on the design of the image area of interest, it cannot be said that the reliability is high even if the standard deviation σ indicating the degree of variation of the density value is calculated from the density values of all the pixels included in the image area of interest.

  Therefore, it is noted that the standard deviation σ indicating the degree of variation in density value is related to the difference between the maximum density value and the minimum density value, and further, the distribution of density values within the calculated noise fluctuation width Wx, y is By assuming that it follows a normal distribution, it is possible to calculate a standard deviation σ x, y that does not depend on the pattern of the image area of interest. Further, since the standard deviation standard deviation σ x, y indicates the degree of variation of the density value, it is used as a noise suppression strength when noise is suppressed.

Specifically, the noise suppression strength calculation unit 71 calculates a value obtained by dividing the noise fluctuation range Wx, y by the σ conversion coefficient SigmaCoef as the standard deviation σ x, y. In order to avoid excessive noise suppression based on the calculated standard deviation σ x, y as the noise suppression strength, a maximum allowable value Level and a minimum allowable value 1 are provided, and as shown in the following equation: The standard deviation σ x, y is calculated.

  The sigmaization coefficient SigmaCoef is, for example, 2.85 (= 1 / (0.5 × 0.7)) when it is assumed that a value corresponding to about 70% of the half value of the noise fluctuation range indicates the standard deviation σ. Become. Further, the inventor of the present application knows that the value of the standard deviation σ is a value of about 10 and is about 16 at the maximum, and the maximum allowable value is desirably set to about 16. However, the values of the sigmaization coefficient SigmaCoef, the maximum allowable value, and the minimum allowable value are not limited to the above-described numerical values, and may be appropriately adjusted as design matters.

  Therefore, in the noise suppression intensity calculation unit 71, the noise fluctuation range for an arbitrary pixel of interest (x, y) based on the noise fluctuation range calculated from the flat image area obtained by the noise fluctuation range correction unit 77. Since Wx, y is calculated and the noise suppression intensity σ x, y is calculated from the noise fluctuation width Wx, y, an outline image region or a pattern having a large density difference between the pixel of interest (x, y) and its surrounding pixels is obtained. In a fine image region or the like, it becomes possible to calculate the noise suppression strength σ x, y that is limited so that pixels necessary for appropriately expressing the contents of the image are not recognized as noise.

  The density-corresponding filter coefficient generation unit 72 dynamically calculates the filter coefficient of the weighted average filter used when the density-corresponding noise suppression processing unit 73 described later performs weighted average filter processing on the density component image data.

More specifically, as shown by the following expression, the filter coefficient Wgti, j for each peripheral pixel (x + i, y + j) in the filter region of a predetermined size centered on the target pixel (x, y) The difference value z i, j between the density value dat x, y and the density value dat x + i, y + j of each peripheral pixel, and the noise suppression strength obtained by the noise suppression strength calculation unit 71, that is, the target pixel (x, y). It is calculated based on a normal distribution function having a standard deviation σ x, y indicating a variation in density value of peripheral pixels in the center region of interest as a standard deviation.

  Note that i, j represents the coordinates of the peripheral pixels when the coordinates (x, y) of the target pixel are regarded as (0,0).

  When the standard deviation σx, y is large in the filter coefficient Wgt i, j, that is, when the variation in the density value of the peripheral pixel in the filter region is large, it is necessary to increase the influence of the peripheral pixel on the target pixel. Therefore, the value of the filter coefficient Wgt i, j becomes large.

  The density filter coefficient generation unit 72 sequentially sets each pixel arranged in the row direction from the leftmost pixel of the first row of density component image data to be processed as a target pixel, and has a predetermined size (for example, about 5 × 5). Repeat the calculation of the filter coefficients for each peripheral pixel in the filter area, and when the calculation processing is completed up to the rightmost pixel, the same processing is performed in the row direction from the leftmost pixel of the second row to the rightmost pixel of the final row. Repeat the process.

  Therefore, the density-corresponding filter coefficient generation unit 72 calculates the filter coefficient Wgt i, j based on the normal distribution function having the noise suppression intensity σ x, y obtained by the noise suppression intensity calculation unit 71 as a standard deviation. The filter coefficient Wgt i, j having smooth characteristics can be calculated.

The density-corresponding noise suppression processing unit 73 uses the weighted average filter formed by the filter coefficients corresponding to each pixel of interest obtained by the density-corresponding filter coefficient generation unit 72, to the left end of the first row of density component image data to be processed. The weighted average filter processing is sequentially repeated with each pixel arranged in the row direction from the first pixel as the target pixel, and when the processing is completed up to the rightmost pixel, the same processing is performed in the row direction from the leftmost pixel of the second row, and finally Repeat the process until the rightmost pixel in the row. The density value of the density component image data subjected to the weighted average filter processing, that is, the density value Yave x, y of the weighted average pixel of the density component image data can be expressed by the following equation.

  Here, nY represents the maximum distance between the target pixel and the peripheral pixels. For example, when the filter size is 5 × 5, nY = 5 ÷ 2≈2.

  Accordingly, the density-corresponding noise suppression processing unit 73 executes the weighted average filter process using the weighted average filter obtained via the noise suppression strength calculation unit 71 and the density-corresponding filter coefficient generation unit 72. By executing the weighted average filtering process that exceeds the fluctuation range Wx, y, it is possible to reduce the problem that the pattern and contour included in the original image are suppressed and erased as noise.

  The chrominance filter coefficient generation unit 75 uses the filter coefficient of the weighted average filter used when the chrominance-corresponding noise suppression processing unit 76 described later performs weighted average filter processing on the chrominance component image data extracted by the density component extraction unit 701. Calculate dynamically.

  More specifically, the color difference filter coefficient generation unit 75, like the density-corresponding filter coefficient generation unit 72, follows each expression in the filter region of a predetermined size centered on the pixel of interest (x, y) according to the equation shown in Equation 4. The filter coefficient Wgti, j for the peripheral pixel (x + i, y + j), the difference value z i, j between the density value dat x, y of the target pixel and the density value dat x + i, y + j of each peripheral pixel, and the noise suppression intensity calculation unit This is calculated based on the noise suppression intensity obtained in 71, that is, the standard deviation σ x, y indicating the variation in the density value of the peripheral pixels in the region of interest centered on the pixel of interest (x, y).

  The color difference filter coefficient generation unit 75 is a predetermined different from the density correspondence filter coefficient generation unit 72 by sequentially setting each pixel arranged in the row direction from the leftmost pixel of the first row of the color difference component image data to be processed as a target pixel. Repeat the calculation of the filter coefficient for each peripheral pixel in the filter area of size (for example, about 9 × 9), and when the calculation processing is completed up to the rightmost pixel, the same processing in the row direction from the leftmost pixel of the second row And repeat the process up to the rightmost pixel in the last row.

  Here, the color difference filter coefficient generation unit 75 calculates filter coefficients for each peripheral pixel in a filter area having a predetermined size (for example, about 9 × 9) different from the density correspondence filter coefficient generation unit 72. This is because the color noise corresponds to the characteristic that it appears with a relatively large pixel size.

  More specifically, in the JPEG method, normally, the YCrCb-converted pixels are DCT-converted for each 8 × 8 block, the obtained frequency components are quantized, and the quantized data arranged by the zigzag scan is Huffman encoded. However, the color difference component image data in each block is thinned out to 1/4 of the luminance component image data before DCT conversion. This is because the color difference component image data is reduced on the basis of the characteristic that the human eye is sensitive to small differences in brightness, but relatively insensitive to differences in hue and saturation.

  Therefore, when granular noise, which is the cause of roughness appearing in a photographic image taken with a digital camera, is divided into luminance noise and color noise, luminance noise appears in units of approximately one pixel, whereas color noise is relatively large. There is a characteristic that it appears as a pixel size and the tendency has a correlation with the block size at the time of DCT conversion.

  Therefore, in order to suppress color noise, it is preferable to set the filter size to a filter size larger than the block size for compression by JPEG. In this embodiment, the filter size is about 9 × 9 or 11 × 11. Set to.

  The color difference filter coefficient generation unit 75 calculates the noise suppression strength determined based on the change of the density component, similarly to the density-corresponding filter coefficient generation unit 72, because the human visual characteristics In addition, noise suppression processing of color difference component image data described later is performed based on the noise suppression strength determined based on the density component change that is more sensitive than the hue component change, and the color change is small after the weighted average filter processing. This is to avoid the generation of an image assimilated with the surroundings.

The color difference-corresponding noise suppression processing unit 76 uses a weighted average filter composed of filter coefficients corresponding to each pixel of interest obtained by the color difference filter coefficient generation unit 75, so that the color difference component image data to be processed is processed at the left end of the first row. Repeat the weighted average filtering process for each pixel arranged in the row direction from the pixel as the target pixel, and when the processing is completed up to the rightmost pixel, the same processing is performed in the row direction from the leftmost pixel of the second row, and the final row Repeat the process up to the rightmost pixel. The weighted average pixel values Cbave x, y, Cave x, y of the color difference component image data subjected to the weighted average filter processing can be expressed by the following equations.

  Cbdat x, y and Crdatave x, y indicate pixel values of the color difference component image data before the weighted average filter process, and nC indicates the maximum distance between the target pixel and the peripheral pixels. For example, when the filter size is 9 × 9, nC = 9 ÷ 2≈4.

The RGB conversion processing unit 74 includes the density component image data after the weighted average filter processing obtained by the density correspondence noise suppression processing unit 73 and the color difference component image after the weighted average filter processing obtained by the color difference correspondence noise suppression processing unit 76. A new luminance / color difference component after noise suppression, which is composed of data, is converted into an RGB color component based on the following equation and output.
R = Y + 1.296048Cb−0.229276Cr
G = Y-0.820087Cb-0.943430Cr
B = Y−0.475961Cb + 1.172706Cr

  Further, as shown in FIG. 4, the density-corresponding noise suppression processing unit 73 uses the noise suppression strength obtained by the noise suppression strength calculation unit 71 as a virtual variance value, and each weight obtained by the density-corresponding noise suppression processing unit 73. A weighted coefficient is calculated based on a normal distribution function having a density difference value between the average pixel and the corresponding target pixel as a variable, and each weighted average pixel and the corresponding target pixel are weighted and added by the weighting coefficient. A correction processing unit 731 for calculating data may be provided.

  More specifically, the difference between the density component image data after the weighted average filter processing obtained by the density-corresponding noise suppression processing unit 73 and the density component image data before the weighted average filter processing indicates the amount of noise removed. In this case, when the noise amount is larger than the noise fluctuation range calculated from the flat image area obtained by the noise fluctuation width correction unit 77, it is considered that the noise suppression process has been performed excessively. It is better to place restrictions.

Therefore, the correction processing unit 731 has a weighted average pixel that is a pixel of density component image data after the weighted average filter processing obtained by the density-corresponding noise suppression processing unit 73, and the weighted average pixel, as shown in the following equation: The density difference value with respect to the target pixel of the density component image data before the weighted average filter processing corresponding to is a variable tmp, and the noise amount is the standard deviation σ x, y indicated by the noise suppression intensity calculated by the noise suppression intensity calculator 71. Assuming that the normal distribution is followed, the weighting coefficient wgt having smooth characteristics is calculated by using the normal distribution.

Further, as shown in the following equation, the correction processing unit 731 does not perform excessive noise suppression processing by performing weighted addition of each weighted average pixel and the corresponding pixel of interest with the calculated weighting coefficient wgt. Then, the density value Yave ′ x, y of the pixel of the corrected density image data suppressed to the above is calculated and the corrected density image data is output to the RGB conversion processing unit 74.

  Therefore, in the correction processing unit 731, the weighted average pixel that is the pixel of the density component image data after the weighted average filter processing obtained by the density-corresponding noise suppression processing unit 73, and before the weighted average filter processing corresponding to the weighted average pixel. Assuming that the density difference value with respect to the target pixel of the density component image data is a variable, the amount of noise follows a normal distribution function with the noise suppression intensity calculated by the noise suppression intensity calculation unit 71 as a virtual variance value, Since the weighted average pixel and the target pixel corresponding to each weighted average pixel are weighted and added with a weighting factor having smooth characteristics, which is a characteristic, noise can be suppressed with an intensity corrected so as not to excessively suppress noise.

  In this case, when the corrected density image data is input from the correction processing unit 731, the RGB conversion processing unit 74 handles the density component image data after the weighted average filter processing obtained by the density corresponding noise suppression processing unit 73. The new luminance / color difference component after noise suppression, which is composed of the density component image data and the color difference component image data after weighted average filter processing obtained by the color difference corresponding noise suppression processing unit 76, is converted into RGB as described above. Convert to color component and output.

  Below, the procedure of each process by the granular noise suppression process part 51 mentioned above is demonstrated based on the flowchart shown in FIG.

  First, when the RGB component data of each pixel constituting the photographic image data is input to the noise fluctuation width specifying unit 70, a noise fluctuation width specifying step (S1) is executed.

  The noise fluctuation range specifying step (S1) includes a density component extraction step (S11), a block characteristic value calculation step (S12), a pixel block allocation step (S13), a pixel block extraction step (S14), and a regression line calculation. It consists of step (S15).

  First, a density component extraction step is executed, each of the input RGB components of each pixel is converted into a YCbCr component, and luminance component image data and color difference component image data are output. The output luminance component image data is handled as density component image data (S11).

  When the output density component image data is input to the block characteristic value calculation unit 702, a block characteristic value calculation step is executed, and the average density is calculated for each pixel block of the density component image data divided into pixel blocks of a predetermined size. A block characteristic value including the value, maximum density value, minimum density value, and density variation is calculated, and the block characteristic value and density component image data divided into pixel blocks are output to the pixel block allocation unit 703 (S12).

  When the density component image data and the block characteristic value for each pixel block are input to the pixel block allocation unit 703, a pixel block allocation step is executed, and the density range of the density component image data is divided into a plurality of sections at predetermined intervals. Based on the input average density value of the pixel block characteristic values, each pixel block of the density component image data is assigned to a corresponding section (S13).

  Subsequently, the pixel block extraction step of the pixel block extraction unit 704 is executed. From the pixel block allocated to each section in the pixel block allocation step (S13), the flattest region having the smallest variation in density of the block characteristic value is obtained. A pixel block is extracted as a representative pixel block (S14).

  Subsequently, the regression line calculation step of the noise fluctuation width specifying unit 70 is executed, and the average density values of the representative pixel blocks indicating the plurality of flat image regions extracted in the pixel block allocation step (S14) are used as explanatory variables, The maximum regression line with the maximum concentration value as the dependent variable and the minimum regression line with each minimum concentration value as the dependent variable are calculated, and the range divided by the maximum regression line and the minimum regression line is a variation in granular noise It is specified as a width (S15).

  Subsequently, the noise fluctuation range correction step of the noise fluctuation range correction unit 77 is executed, and the slope and Y axis intercept of the minimum regression line calculated in the noise fluctuation range specifying step S1 and the correlation coefficient at the time of calculating the regression line are calculated. Based on this, the maximum regression line is corrected, and the range defined by the corrected maximum regression line and minimum regression line is specified as the fluctuation range of granular noise (S16).

  Subsequently, a noise suppression intensity calculation step of the noise suppression intensity calculation unit 71 is executed, and arbitrary attention is given to the density component image data based on each regression line calculated from the flat image area in the noise fluctuation range correction step (S16). The noise fluctuation width Wx, y for the pixel (x, y) is calculated, and the noise suppression intensity σ x, y is calculated from the noise fluctuation width Wx, y (S2).

  Subsequently, the density-corresponding filter coefficient generation step of the density-corresponding filter coefficient generation unit 72 is executed, and the noise suppression obtained in the density difference value z i, j between the target pixel and the surrounding pixels and the noise suppression intensity calculation step (S2). Based on the intensity σ x, y, the filter coefficient Wgti, j of the weighted average filter having a size corresponding to the density component image data is dynamically calculated (S3).

  Subsequently, the density-corresponding noise suppression processing step of the density-corresponding noise suppression processing unit 73 is executed, and the weight constituted by the filter coefficient Wgti, j corresponding to each target pixel obtained in the density-corresponding filter coefficient generation step (S3). When the average filter repeats the weighted average filter process using the pixels arranged in the row direction from the leftmost pixel in the first row of the density component image data to be processed as the target pixel, and the processing ends up to the rightmost pixel. The same processing is performed in the row direction from the pixel on the left end of the second row, and the processing is repeated until the pixel on the right end of the last row, and the density value of the density component image data subjected to the weighted average filter processing, that is, the luminance component image data The density value Yave x, y of the weighted average pixel is calculated (S4).

  Further, the density component image data and the color difference component image data extracted in the density component extraction step (S11), and the noise suppression strength σ x, y calculated in the noise suppression strength calculation step (S2) are used as the color difference corresponding filter coefficient generation unit. 75, the color difference correspondence filter coefficient generation step is executed, and the density difference value z i, j between the target pixel and the surrounding pixels and the noise suppression strength σ x, j obtained in the noise suppression strength calculation step (S2). Based on y, the filter coefficient Wgti, j of the weighted average filter having the size corresponding to the color difference component image data is dynamically calculated (S5).

  Subsequently, the color difference-corresponding noise suppression processing step of the color difference-corresponding noise suppression processing unit 76 is executed, and the weighting composed of the filter coefficients Wgti, j corresponding to each target pixel obtained in the color difference-corresponding filter coefficient generation step (S5). When the average filter repeats the weighted average filter process using the pixels arranged in the row direction from the leftmost pixel in the first row of the color difference component image data to be processed as the target pixel, and the processing ends up to the rightmost pixel. The same processing is performed in the row direction from the leftmost pixel of the second row, and the processing is repeated up to the rightmost pixel of the final row, and the weighted average pixel value Cbave x, y, Cave x, y is calculated (S6).

  Further, when the density-corresponding noise suppression processing unit 73 includes the correction processing unit 731, the density-corresponding noise suppression processing step (S 4) further includes a correction processing step (S 41).

  More specifically, the density value of the density component image data subjected to the weighted average filter processing in the density-corresponding noise suppression processing step (S4), that is, the density value Yave x, y of the weighted average pixel of the luminance component image data, and the density component extraction. When the density component image data extracted in step (S11) is input to the correction processing unit 731, the correction processing step is executed, and the noise suppression strength σ x, y obtained in the noise suppression strength calculation step (S2) is standardized. The weighting coefficient wgt is calculated based on a normal distribution function that uses the difference in density as a variable tmp as the deviation and the density difference value between each weighted average pixel obtained in the density-corresponding noise suppression processing step (S4) and the corresponding target pixel.

  Further, in the correction processing step, the corrected density image data Yave, which is weighted and added to each weighted average pixel and the corresponding pixel of interest with the calculated weighting coefficient wgt, is suppressed so as not to perform excessive noise suppression processing. 'X, y is calculated, and the corrected density image data Yave'x, y is output to the RGB conversion processing unit 74 (S41).

  Subsequently, the density component image data subjected to the weighted average filter processing in the density-corresponding noise suppression processing step (S4), or the corrected density image data calculated in the correction processing step (S41), and the color difference corresponding noise suppression processing step (S6). When the color difference component image data after the weighted average filter processing obtained in (4) is input to the RGB conversion processing unit 74, the RGB conversion processing step is executed, and the input density component image data or the corrected density image is input. A new luminance / color difference component after noise suppression composed of the data and the color difference component image data is converted into an RGB color component and output, and the granular noise suppression processing by the granular noise suppression processing unit 51 is completed. (S8).

  In each of the steps described above, the input / output data between the steps is stored in the memory 41, and each step stores the data stored in the memory 41 without directly inputting / outputting data between the steps. You may comprise so that it may access.

  Each processing by the granular noise suppression processing unit 51 described above is realized by executing a photographic image processing program of the present invention installed in a hard disk provided in the controller 33.

  That is, a photographic image processing program for performing noise suppression processing based on a fluctuation range of granular noise included in photographic image data, a density component extraction step S11 for extracting density component image data from photographic image data, and a density component Block characteristics for dividing the density component image data extracted in the extraction step S11 into pixel blocks of a predetermined size and calculating block characteristic values including average density values, maximum density values, minimum density values, and density variations for each pixel block. From the value calculation step S12, the pixel range assignment step S13 for dividing the density range into a plurality of sections at predetermined intervals, and assigning each pixel block to the corresponding section based on the average density value, and the pixel block assigned to each section Pixel block that extracts the pixel block with the smallest density variation as the representative pixel block A maximum regression line and each minimum density value with each average density value of the plurality of representative pixel blocks extracted in the pixel block extraction step S14 as explanatory variables and each maximum density value as a dependent variable. A noise fluctuation range specifying step S1 for calculating a minimum regression line having a dependent variable as the dependent variable, and specifying a range defined by the maximum regression line and the minimum regression line as a fluctuation range of granular noise, and a noise fluctuation range specifying step Noise that corrects the maximum regression line based on the minimum regression line calculated in S1 and specifies a range defined by the corrected maximum regression line and minimum regression line as the fluctuation range of granular noise The variation range correction step S16 is installed via a storage medium such as a CD or a DVD in which a photographic image processing program for causing the computer to execute is stored.

  Further, based on each regression line obtained in the noise fluctuation width correction step S16, the noise fluctuation width Wx, y for an arbitrary pixel of interest (x, y) is obtained, and the noise suppression intensity σ is obtained from the noise fluctuation width Wx, y. Noise suppression strength calculation step S2 for calculating x, y, density difference value z i, j between the target pixel (x, y) and surrounding pixels, and noise suppression strength σ x obtained in noise suppression strength calculation step S2. , y based on the density component filter data generation step S3 for dynamically calculating the filter coefficient Wgti, j of the weighted average filter of the size corresponding to the density component image data, and the density correspondence filter coefficient generation step S3. Based on the filter coefficient Wgti, j corresponding to each pixel of interest, the density-corresponding noise suppression processing step S4 for performing weighted average filtering on the density component image data is performed. Image processing program executed by a computer is, it may be installed the program through a storage medium such as stored CD or DVD.

  Furthermore, it corresponds to the color difference component image data based on the density difference value z i, j between the target pixel (x, y) and the surrounding pixels and the noise suppression strength σ x, y obtained in the noise suppression strength calculation step S2. Based on the filter coefficient Wgti, j corresponding to each pixel of interest obtained in the color difference corresponding filter coefficient generation step S5 and the color difference corresponding filter coefficient generation step S5, which dynamically calculate the filter coefficient of the weighted average filter of the selected size, A photographic image processing program that causes the computer to execute color difference-corresponding noise suppression processing step S6 for performing weighted average filtering on the color difference component image data may be installed via a storage medium such as a CD or a DVD in which the program is stored. Absent.

  Further, based on a normal distribution function in which the noise suppression strength σ x, y is a virtual variance value, and the density difference value between each weighted average pixel obtained in the density-corresponding noise suppression processing step S4 and the corresponding pixel of interest is a variable tmp. A photographic image processing program for calculating a weighting factor wgt and causing the computer to execute correction processing step S41 for calculating corrected density image data by weighted addition of each weighted average pixel and the corresponding target pixel with the weighting factor wgt. The program may be installed via a storage medium such as a CD or DVD that stores the program.

  As the density variation included in the block characteristic value of the pixel block, the average density value of each pixel block is set as a threshold value, and the high density side average density value that is an average density value of pixels higher than the threshold value is compared with the threshold value. Although the case where the value calculated by the difference calculation with the low density side average density value which is the average density value of the low density pixel has been described, the density variation applicable to the present invention is limited to such a value. Rather, it may be a value that represents the variation in the density value of each pixel having the pixel group constituting the pixel block as a population by a statistical method or the like, and adopts a standard deviation, an average absolute deviation, a range, and the like. It is also possible.

  In addition, since there is no actual pixel in the end area of the processing target image data, a dummy pixel having the same pixel value as that of the end pixel is generated and processed. However, the effective image size actually printed is the original image. Since the size is slightly smaller than the size, it does not affect the print image.

The conversion formula used in the above-described density component extraction unit 701 and RGB conversion processing unit 74 derives the luminance component as the average value of the RGB color components. For example, the conversion formula for the Y component is the following formula: As shown by, it may be appropriately changed as a design matter.
Y = 0.29900R + 0.58700G + 0.11400B

  However, data obtained as a result of conversion from the RGB color component to the luminance color difference component in the density component extraction unit 701 is input to the RGB conversion processing unit 74, and the result of conversion into the RGB color component is the same as the original RGB color component. Needless to say, it is necessary to change the conversion formula to be consistent.

  Hereinafter, the second embodiment will be described as an embodiment different from the first embodiment.

  In the first embodiment, the noise variation correction unit 77 corrects the maximum regression line when the gradient of the maximum regression line calculated by the noise variation identification unit 70 is larger than the gradient of the minimum regression line. However, when the correlation coefficient of the maximum regression line exceeds a predetermined threshold, the above-described maximum regression line may be corrected.

  For example, when calculating the noise fluctuation range of an image whose design is significantly biased in the dark part and the bright part, the pixel block allocating unit 703 increases the number of pixel blocks allocated to a specific density range. Although it can be calculated appropriately, it is difficult to say that the amount of noise is calculated appropriately in the density range where the number of allocated pixel blocks is small. That is, when the correlation coefficient r of the maximum regression line calculated by the noise fluctuation range specifying unit 70 is low, it is difficult to say that the calculated noise fluctuation range is appropriate.

  Therefore, in this case, since the noise fluctuation range correction unit 77 specifies the noise fluctuation range by correcting the maximum regression line when the correlation coefficient r of the maximum regression line exceeds a predetermined threshold, as described above. It is possible to avoid calculating a noise fluctuation range that is difficult to say.

  Hereinafter, a third embodiment will be described as an embodiment different from the first and second embodiments.

  In addition to the configuration of the first or second embodiment, the noise fluctuation range correction unit 77 reduces the predetermined size of the pixel block when the slope of the maximum regression line is larger than the minimum regression line, and after the reduction Based on the image block of the predetermined size, the maximum fluctuation line and the minimum regression line are recalculated via the noise fluctuation range specifying unit 70, and the recalculated value is calculated based on the recalculated minimum regression line. The maximum regression line may be corrected.

  More specifically, the noise fluctuation range correction unit 77 changes the setting so as to reduce the size of the pixel block used by the noise fluctuation range specifying unit 70 when the slope of the maximum side regression line is larger than the minimum side regression line. Based on an image block of a predetermined size after reduction through a block characteristic value calculation unit 702, a pixel block allocation unit 703, a pixel block extraction unit 704, and a regression line calculation unit 705 provided in the noise fluctuation range specification unit 70. The maximum regression line and the minimum regression line are recalculated.

  Therefore, comparing the initially calculated regression line with the recalculated regression line, the recalculated regression line is calculated based on more pixel blocks. An appropriately determined noise fluctuation range can be obtained.

  In addition, in the pixel block of the initial size, when both the pixel indicating the outline portion and the pixel indicating the background portion are included in the pixel block, the density difference between the two pixels indicating the pattern is calculated as noise. However, by reducing the size of the pixel block, it is possible to divide into pixel blocks that include only the pixels in the contour and background portions. Calculation as noise can be avoided.

  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.

External view of photographic image processing device Illustration of photo printer Functional block diagram of photographic image processing device Functional block diagram of the granular noise suppression processing unit It is explanatory drawing of a noise fluctuation width specific step, (a) is explanatory drawing which shows the density component image data divided | segmented into the pixel block of predetermined size, (b) is explanatory drawing which shows each pixel block of density component image data, (C) is explanatory drawing which shows an example which allocated the pixel block to the density range divided | segmented into the some division, (d) is explanatory drawing which shows an example which specifies a noise fluctuation range. It is explanatory drawing regarding correction | amendment of the maximum side regression line, (a) is explanatory drawing of the 1st correction | amendment which correct | amends inclination, (b) is explanatory drawing of the 2nd correction | amendment which correct | amends a 1st correction | amendment and an intercept, ( c) is an explanatory diagram showing that the use of the noise fluctuation range is limited without correction. It is explanatory drawing of distribution of the density value in a predetermined image area, (a) is explanatory drawing which shows distribution of the normalized density value of the image area containing only a flat image area, (b) is flat image area Explanatory drawing which shows distribution of normalized density value of the image area including both the image area and the contour image area Flowchart for explaining an example of the processing procedure of the granular noise suppression processing unit

Explanation of symbols

1: Photographic image processing device 51: Granular noise suppression processing unit 70: Noise fluctuation width specifying unit 71: Noise suppression intensity calculation unit 72: Density corresponding filter coefficient generation unit 73: Density corresponding noise suppression processing unit 74: RGB conversion processing unit 75 : Color difference corresponding filter coefficient generation unit 76: color difference corresponding noise suppression processing unit 77: noise variation range correction unit 701: density component extraction unit (noise variation range identification unit)
702: Block characteristic value calculation unit (noise fluctuation width specifying unit)
703: Pixel block allocation unit (noise fluctuation width specifying unit)
704: Pixel block extraction unit (noise fluctuation width specifying unit)
705: regression line calculation unit (noise fluctuation range specifying unit)
731: Correction processing unit (density-corresponding noise suppression processing unit)
S1: Noise fluctuation width identification step S2: Noise suppression intensity calculation step S3: Density correspondence filter coefficient generation step S4: Density correspondence noise suppression processing step S5: Color difference correspondence filter coefficient generation step S6: Color difference correspondence noise suppression processing step S8: RGB conversion Processing Step S11: Density Component Extraction Step (Noise Fluctuation Range Specifying Step)
S12: Block characteristic value calculating step (noise fluctuation range specifying step)
S13: Pixel block allocation step (noise fluctuation width specifying step)
S14: Pixel block extraction step (noise fluctuation width specifying step)
S15: regression line calculation step (noise fluctuation range specifying step)
S16: Noise fluctuation range correction step S41: Correction processing step (density-corresponding noise suppression processing step)

Claims (12)

A photographic image processing method for performing noise suppression processing based on a fluctuation range of granular noise included in photographic image data,
A density component extraction step for extracting density component image data from the photographic image data;
The density component image data extracted in the density component extraction step is divided into pixel blocks of a predetermined size, and block characteristic values including average density value, maximum density value, minimum density value, and density variation are calculated for each pixel block. A block characteristic value calculating step,
In a two-dimensional coordinate system including an X axis indicating a density range and a Y axis indicating the block characteristic value, the density range is divided into a plurality of sections at predetermined intervals, and each pixel block is associated based on the average density value. A pixel block assignment step for assigning to the sections;
A pixel block extraction step of extracting, as a representative pixel block, a pixel block having the minimum density variation from the pixel blocks assigned to each section;
The average density value of the plurality of representative pixel blocks extracted in the pixel block extraction step is used as an explanatory variable, and the maximum regression line having each maximum density value as a dependent variable and each minimum density value as a dependent variable. A noise fluctuation range specifying step of calculating a minimum regression line and specifying a range defined by the maximum regression line and the minimum regression line as a fluctuation range of the granular noise;
Based on the minimum-side regression line calculated in the noise fluctuation range specifying step, the maximum-side regression line is corrected, and a range defined by the corrected maximum-side regression line and the minimum-side regression line is A noise fluctuation range correction step that is specified as a fluctuation range of granular noise,
A photographic image processing method.   The noise fluctuation range correction step performs the first correction to match the slope of the maximum regression line with the slope of the minimum regression line, and the maximum regression line and the minimum regression line after the first correction The photographic image processing method according to claim 1, wherein a range divided by is specified as a fluctuation range of the granular noise.   In the noise fluctuation range correction step, when the Y-axis intercept of the maximum-side regression line after the first correction is smaller than the Y-axis intercept of the minimum-side regression line, the maximum side before and after the first correction Based on the ratio of the Y-axis intercept of the regression line, a second correction for correcting the Y-axis intercept of the first corrected maximum regression line is performed, and the second corrected maximum regression line and the The photographic image processing method according to claim 2, wherein a range defined by a minimum-side regression line is specified as a fluctuation range of the granular noise.   The photographic image processing method according to claim 1, wherein the noise fluctuation range correction step corrects the maximum regression line when a correlation coefficient of the maximum regression line exceeds a predetermined threshold.   The noise fluctuation range correction step reduces the predetermined size of the pixel block when the slope of the maximum regression line is larger than the minimum regression line, and based on the pixel block having the predetermined size after the reduction The maximum fluctuation line and the minimum regression line are recalculated through the noise fluctuation range specifying step, and the recalculated maximum regression line is calculated based on the recalculated minimum regression line. The photographic image processing method according to claim 1, wherein correction is performed.   The density variation is a high density side average density value that is an average density value of pixels having a higher density than the threshold value and an average density value of pixels having a density lower than the threshold value, with the average density value of each pixel block as a threshold value. 6. The photographic image processing method according to claim 5, wherein the photographic image processing method is a value calculated by calculating a difference from the low density side average density value. Based on each regression line obtained in the noise fluctuation range correction step, obtain a noise fluctuation range for any target pixel, and calculate a noise suppression intensity from the noise fluctuation range,
A filter coefficient of a weighted average filter having a size corresponding to the density component image data is dynamically calculated based on the density difference value between the target pixel and surrounding pixels and the noise suppression intensity obtained in the noise suppression intensity calculation step. A density-corresponding filter coefficient generation step;
Based on the filter coefficient corresponding to each target pixel obtained in the density-corresponding filter coefficient generation step, the density-corresponding noise suppression processing step for performing weighted average filter processing on the density component image data,
The photographic image processing method according to claim 1, further comprising: A filter coefficient of a weighted average filter having a size corresponding to the color difference component image data is dynamically calculated based on the density difference value between the target pixel and surrounding pixels and the noise suppression intensity obtained in the noise suppression intensity calculation step. A color difference corresponding filter coefficient generation step;
Based on the filter coefficient corresponding to each pixel of interest obtained in the color difference corresponding filter coefficient generation step, a color difference corresponding noise suppression processing step for performing weighted average filter processing of the color difference component image data,
The photographic image processing method according to claim 7, further comprising: A photographic image processing program for performing noise suppression processing based on a fluctuation range of granular noise included in photographic image data,
A density component extraction step for extracting density component image data from the photographic image data;
The density component image data extracted in the density component extraction step is divided into pixel blocks of a predetermined size, and block characteristic values including average density value, maximum density value, minimum density value, and density variation are calculated for each pixel block. A block characteristic value calculating step,
A pixel block allocating step of dividing the density range into a plurality of sections at predetermined intervals, and allocating each pixel block to a corresponding section based on the average density value;
A pixel block extraction step of extracting, as a representative pixel block, a pixel block having the minimum density variation from the pixel blocks assigned to each section;
The average density value of the plurality of representative pixel blocks extracted in the pixel block extraction step is used as an explanatory variable, and the maximum regression line having each maximum density value as a dependent variable and each minimum density value as a dependent variable. A noise fluctuation range specifying step of calculating a minimum regression line and specifying a range defined by the maximum regression line and the minimum regression line as a fluctuation range of the granular noise;
Based on the minimum-side regression line calculated in the noise fluctuation range specifying step, the maximum-side regression line is corrected, and a range defined by the corrected maximum-side regression line and the minimum-side regression line is A noise fluctuation range correction step that is specified as a fluctuation range of granular noise,
A photographic image processing program that causes a computer to execute. Based on each regression line obtained in the noise fluctuation range correction step, obtain a noise fluctuation range for any target pixel, and calculate a noise suppression intensity from the noise fluctuation range,
A filter coefficient of a weighted average filter having a size corresponding to the density component image data is dynamically calculated based on the density difference value between the target pixel and surrounding pixels and the noise suppression intensity obtained in the noise suppression intensity calculation step. A density-corresponding filter coefficient generation step;
Based on the filter coefficient corresponding to each target pixel obtained in the density-corresponding filter coefficient generation step, the density-corresponding noise suppression processing step for performing weighted average filter processing on the density component image data,
The photographic image processing program according to claim 9, further comprising: A filter coefficient of a weighted average filter having a size corresponding to the color difference component image data is dynamically calculated based on the density difference value between the target pixel and surrounding pixels and the noise suppression intensity obtained in the noise suppression intensity calculation step. A color difference corresponding filter coefficient generation step;
Based on the filter coefficient corresponding to each pixel of interest obtained in the color difference corresponding filter coefficient generation step, a color difference corresponding noise suppression processing step for performing weighted average filter processing of the color difference component image data,
The photographic image processing program according to claim 10, further comprising:   A photographic image processing apparatus in which the photographic image processing program according to any one of claims 9 to 11 is installed, and a fluctuation range of granular noise included in the photographic image data by executing the photographic image processing program. Image processing apparatus for performing noise suppression processing based on the above.