JP2009065402A - Gradation correction processing method, program for executing the same and gradation correction processing device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To obtain a high quality binary image by solving a moire problem and an edge blur problem due to a tone jump. <P>SOLUTION: This gradation correction processing method for correcting the gradation of an inputted binary image comprises the steps for: detecting a change point pixel DE of a binary image; setting a density measurement region CE having the size of a screen region of 50 lines/inch centered on the detected change point pixel DE; measuring density Rin within the set density measurement region; calculating randomization probabilities K and K' by comparing the measured density value Rin with a target density value Rout; and changing the values of the change point pixel DE according to the calculated randomization probabilities K and K'. The gradation correction processing method sets the density measurement region CE in each change point pixel DE. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a tone correction processing method for a binary image, and more particularly to a method for correcting the tone information of an image in accordance with an input tone correction curve for an input binary image.

  Conventionally, editing such as reading a binary image such as a photograph with an input device such as a scanner and correcting the gradation (density) of the read image has been widely performed. When correcting the gradation of an image, in order to avoid losing the reproducibility of the light and shade, it is possible to accurately detect the light and shade of the original image when reading a photograph or the like and to accurately grasp the light and shade information on the image data. Desired.

  The density of binary images such as photographs is generally expressed by the size of halftone dots, and the sharpness of the density expression is determined by the density of the halftone dots, that is, the number of screen lines of the halftone dots used at the time of image creation. It is done. Such a halftone dot is recognized as a black area when read by the input device, and a portion corresponding to the halftone dot is output from the input device as an aggregate of black pixel data.

  Therefore, theoretically, it is possible to determine the density of the original image by detecting the black pixel portion with reference to the output data of the input device. However, in actual image processing, although it is possible to recognize the size of each halftone dot, it is not easy to derive screen information (screen center position and screen size) used when creating the original image. Therefore, it is difficult to create accurate density information.

  Therefore, in the conventional gradation correction processing apparatus, after the input binary image is divided into blocks each having a predetermined size, the number of ON pixels (black pixels) in the block is calculated for each divided block. The average density value of the block is obtained by dividing by a number (see, for example, Patent Document 1). In this method, as shown in FIG. 10, the size of each block is determined to include a large number of halftone dots, and gradation correction processing is performed using the average density of the block. According to this method, since the density of the original image is not measured for each halftone dot, but the density is measured in units of blocks, it is possible to input without obtaining the screen line number or screen angle of each halftone dot. It is possible to perform gradation correction by acquiring density information of a binary image.

JP 2006-72173 A

  In a conventional density measurement method, for a uniformly distributed area, as the density measurement area size (size of each block) increases, the obtained average density approximates the actual density of the original image. However, for areas where multiple densities are mixed, the density information becomes smoother as the size of the density measurement area increases. And edge blurring problems may occur.

  Note that the tone jump means that the density is averaged within a certain area with respect to the continuous halftone dot density on the original image, so that the continuity of gradation is lost at the boundary of the area. When the difference in the measured density between the areas to be processed is large (to the extent that the difference in density between adjacent areas can be recognized with eyes), a moire problem occurs. Also, edge blur means that when a plurality of density regions are mixed in a measurement region, a light / dark boundary (edge portion) is blurred due to density averaging.

  Therefore, the present invention has been made in view of the above-described problems in the prior art, and is a gradation correction capable of solving the moire problem and the edge blurring problem caused by tone jump and obtaining a high-quality binary image. An object is to provide a processing method and the like.

  To achieve the above object, the present invention provides a gradation correction processing method for correcting gradation of an input binary image, the step of detecting change point pixels of the binary image, and the detected change. Setting a density measurement area having a size corresponding to the screen area size of the maximum number of screen lines that can be used in the binary image with reference to a point pixel, and measuring the density in the set density measurement area And a step of performing gradation correction using the measured density, wherein a plurality of density measurement areas are set along the outer periphery of a halftone dot.

  According to the present invention, the edge portion of the density change existing in the input binary image can be followed, and at the same time, the tone jump problem and the moire problem can be solved, and a high-quality binary image is obtained. It becomes possible.

  In the gradation correction processing method, the density measurement region can be set so that the change point pixel is a center position. According to this, since not only the black pixel of the target halftone dot but also a part of the black pixel of the halftone dot located around the target pixel can be included in the density measurement region, It becomes possible to improve the continuity between points.

  In the gradation correction processing method, the length of one side of the density measurement region can be set to 1 to 2 times the length of one side of the screen region having the maximum screen line number. According to this, since black pixels with halftone dots located in the vicinity can be appropriately included in the density measurement region, continuity between halftone dots is improved while maintaining the sharpness of the image after gradation correction. It becomes possible to make it.

  In the gradation correction processing method, the density measurement region can be set for each halftone dot change point pixel. According to this, since density values can be obtained for all change point pixels present in each halftone dot, it is possible to effectively prevent moire caused by density measurement errors.

  In the gradation correction processing method, the gradation correction can be performed by converting the value of the change point pixel using a randomization correction process.

  In the gradation correction processing method, a randomization probability is obtained using a deviation between the measured density and the target density, and whether or not the change point pixel value is converted by comparing the randomization probability with a uniform random number. Can be determined. According to this, since the change point pixels whose values have been changed can be discretely distributed in the edge portions of the halftone dots, it is possible to perform gradation adjustment without impairing the naturalness of the gradation. Become.

  In the gradation correction processing method, the change point pixel can be a contour point pixel and / or a boundary point pixel.

  Further, the present invention is a program for executing a gradation correction process for correcting the gradation of an input binary image, and for executing any one of the gradation correction processing methods described above. It is characterized by. According to the present invention, similar to the above-described invention, the edge portion of the density change existing in the input binary image can be tracked, and at the same time, the tone jump problem and the moire problem can be solved. A value image can be obtained.

  Furthermore, the present invention is a gradation correction processing device for correcting the gradation of an input binary image, comprising a change point detection means for detecting change point pixels of the binary image, and the change point detection means. Measurement in which a plurality of density measurement areas having a size corresponding to the screen area size of the maximum number of screen lines that can be used in the binary image are set along the outer periphery of the halftone dot, using the detected change point pixel as a reference. Area setting means, density measuring means for measuring the density in the density measuring area set by the measurement area setting means, and tone correcting means for performing tone correction using the density measured by the density measuring means It is characterized by providing. According to the present invention, similar to the above-described invention, the edge portion of the density change existing in the input binary image can be tracked, and at the same time, the tone jump problem and the moire problem can be solved. A value image can be obtained.

  As described above, according to the present invention, it is possible to solve the moire problem due to tone jump and the edge blur problem and obtain a high-quality binary image.

  Next, embodiments of the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a block diagram showing an embodiment of a gradation correction processing apparatus according to the present invention. The gradation correction processing apparatus 1 is roughly divided into a change point detection unit 2, a density measurement unit 3, and The randomization correction unit 4 and the random number generation unit 5 are configured.

  Note that the memory 6 in the figure stores image data output from an input device (not shown) such as a scanner, or processes processed by the change point detection unit 2, the density measurement unit 3, and the randomization correction unit 4. It is a storage medium for storing values and image data. In the gradation correction processing apparatus 1, the change point detection unit 2, the density measurement unit 3, the randomization correction unit 4 and the random number generation unit 5 do not necessarily need to be configured by hardware, and part or all of them are included. It may be configured by software (program).

  The change point detection unit 2 detects a change point in the image using the input binary image data output from the input device. Here, the change points to be detected include a contour point image that is a black pixel and a boundary point pixel that is a white pixel. When detecting a contour point, an arbitrary black pixel in the input binary image is set as a target pixel, and for example, a 3 × 3 pixel range centered on the target pixel is referred to. As shown in FIG. 2A, if at least one white pixel is present in the eight pixels located around the target pixel, the target pixel is detected as a contour point. On the other hand, when detecting a boundary point, an arbitrary white pixel in the input binary image is set as a target pixel, and if there is at least one black pixel in the neighboring eight pixels as shown in FIG. The pixel of interest is detected as a boundary point.

The density measuring unit 3 obtains the density of the change point pixel with reference to the change point detected by the change point detection unit 2. When determining the density of the change point pixel, as shown in FIG. 3A, the maximum (coarse) screen line number that can be used in the original image with the change point pixel DE of the target halftone dot H ₀ as the center. A density measurement region CE having a size slightly larger than the size of the screen region SE is set.

  Usually, a halftone dot screen line number of a halftone dot image is 50 lines / inch to 300 lines / inch. However, in an actual printed matter, the halftone dot screen line number is used for printing on paper, ink, printing equipment, and the like. Depending on conditions, it is 60 lines / inch or more. Therefore, the size of the density measurement region CE is preferably a size corresponding to a screen region of 50 lines / inch, which is slightly larger than the screen region of 60 lines / inch. However, the size of the density measurement region CE is not limited to the size of 50 lines / inch. The length of one side of the screen region SE with the maximum number of screen lines is L, and the length of one side of the density measurement region CE. Is L ′, the length L ′ can be 1 to 2 × L (see FIG. 3B).

The density measurement region CE mainly includes black pixels corresponding to the target halftone dot H ₀ , but the change point pixel DE existing at the edge of the target halftone dot H ₀ is the center of the density measurement region CE. Therefore, some of the black pixels corresponding to the surrounding halftone dots H ₁ , H ₂ , and H ₃ are also included (see FIG. 3A). Note that the position of the change point pixel DE and the center position of the density measurement region CE do not need to be completely coincident. If the change point pixel DE is positioned substantially at the center of the density measurement region CE, the positions of the two are slightly different. It may be shifted.

  In the density measurement unit 3, when the density measurement area CE is set, the number of black pixels is detected for the set density measurement area CE, and the detected number of black pixels is divided by the total number of pixels in the density measurement area CE. Measure the concentration.

  Returning to FIG. 1, the randomization correction unit 4 is for performing a randomization correction process on the change point pixel density measured by the density measurement unit 3. In the randomization correction process, first, the measured change point pixel density is compared with the target density on the gradation correction curve to obtain a randomization probability representing the strength of randomization correction for the change point pixel. Thereafter, it is determined whether or not to change the value of the change point pixel based on the obtained randomization probability, and the conversion from the black pixel to the white pixel or the conversion from the white pixel to the black pixel is performed according to the determination result.

  The random number generator 5 is for generating uniform random numbers. Here, the uniform random number means that each generated value (any value between 0.0 and 1.0) is the same when the entire generated value is 0.0 to 1.0. It means a random number generated with frequency.

  Next, a tone correction processing method using the tone correction processing apparatus 1 will be described with reference to FIGS. Here, an example will be described in which an AM screen plate making for newspaper printing is read and gradation of the read binary image is corrected. Further, it is assumed that the resolution at the time of plate making reading (resolution of the input binary image) is 1200 dpi.

In the gradation correction process, as shown in FIG. 4, first, the read binary image of the plate making is read from the memory 6 (see FIG. 1), and change point pixels are detected for the entire binary image (step). S1). Next, an arbitrary pixel is selected from the detected change point pixels (step S2), a density measurement region CE centered on the selected change point pixel DE ₀ is set, and a change point pixel density Rin is calculated ( Step S3, see FIG. 5).

In calculating the change point pixel density Rin, first, the size of the density measurement area CE, that is, the size of the screen area of 50 lines / inch is converted into the number of pixels S ² in accordance with the resolution of the input binary image. When the resolution of the input binary image is 1200 dpi, the number S of pixels in the main scanning direction and the sub-scanning direction of the screen area of 50 lines / inch is 1200/50 = 24 pixels, respectively. The number of pixels S ² is 24 × 24 (see FIG. 5).

Next, a density measurement region CE with the number of pixels S ² = 24 × 24 is set centering on the change point pixel DE ₀ selected in step S2, and a ratio Rin occupied by black pixels in the density measurement region CE is calculated. If the total number of pixels in the density measurement region CE is PN and the number of black pixels in the measurement region is BN, PN = 576 and BN = 70 in the example shown in FIG. From Rin = BN / PN, it is about 0.122 (12.2%).

  Returning to FIG. 4, when the calculation of the change point pixel density Rin is completed, the calculated change point pixel Rin is substituted into the gradation correction curve shown in FIG. 6 to obtain the target density Rout (step S4). In the gradation correction curve shown in FIG. 6, the target density Rout is 10.0% when the change point pixel density Rin≈12.2%.

Next, it is determined whether or not the change point pixel DE ₀ selected in step S2 in FIG. 4 is a black pixel (step S5). If the change point pixel DE ₀ is a black pixel, the calculated change point pixel DE ₀ is determined. It is determined whether or not the density Rin is larger than the target density Rout (step S6).

  As in the above example (change point pixel density Rin≈12.2%, target density Rout = 10.0%), when the change point pixel density Rin is greater than the target density Rout, Since the number of black pixels should be reduced, a randomization probability K corresponding to the reduction rate is calculated (step S7).

Specifically, first, using the following formula (1), the number N of pixels for converting the black pixels in the density measurement region CE into white pixels is obtained,
N = (Rin−Rout) × PN (Formula 1)
At the same time, the total number PS of change points (contour point pixels) of black pixels existing in the density measurement region CE is acquired. For example, when only contour points are extracted from the input binary image of FIG. 5, the result is as shown in FIG. 7, and the total number PS of black pixel change points in this case is 52.

Next, an average conversion rate σ for converting a contour point pixel in the density measurement region CE into a white pixel is obtained using the following equation (2).
σ = N / PS (Formula 2)

Finally, the obtained average conversion rate σ is replaced with the randomization probability K of the change point pixel DE ₀ (contour point pixel) at the center of the density measurement region CE,
K = σ = ((Rin−Rout) × PN) / PS (Formula 3)
A randomization probability K (≈0.244) for the change point pixel DE _{0 is} obtained.

  Returning to FIG. 4, when the calculation of the randomization probability K is finished, a uniform random number is generated from the random number generation unit 5 (see FIG. 1) (step S8), and the generated random number K is compared with the randomization probability K to generate the generated value Q. Is acquired (step S9). In the acquisition of the generated value Q, if the generated random number is less than or equal to the randomization probability K, the generated value Q = 1, and if the generated random number is greater than the randomized probability K, the generated value Q = 0.

  Here, since the probability that a value of 0.24 or less is output from the random number generator 5 is 24%, the generated value Q = 1 when the randomization probability K is 0.24 (≈0.244). Probability is 24%. Therefore, the generated value Q takes the value “1” with a probability that matches the randomization probability K.

  On the other hand, when the change point pixel density Rin is equal to or less than the target density Rout (step S6: No), it is not necessary to reduce the number of black pixels in the density measurement region CE, and therefore the randomization probability K is set to zero. At the same time, the generated value Q is set to 0 (steps S10 and S11).

When the generated value Q is acquired, it is determined whether or not the acquired generated value Q is 1 (step S12). If the generated value Q is 1, the black pixel of the change point pixel DE ₀ is changed to a white pixel. Conversion is performed (step S13). On the other hand, when the generated value Q is 0, the change point pixel DE ₀ is maintained as a black pixel (step S14). The converted pixel data is recorded in the memory 6 (see FIG. 1), whereby the change point pixel DE ₀ is converted from a black pixel to a white pixel with a probability according to the randomization probability K.

If the change point pixel DE ₀ is a white pixel (step S5: No), the change point pixel DE ₀ is a boundary point pixel, and therefore the change point pixel density Rin calculated in step S3 is It is determined whether it is smaller than the target density Rout (step S15). When the change point pixel density Rin is smaller than the target density Rout, the number of black pixels in the density measurement region CE should be increased. Therefore, the randomization probability K ′ is calculated by the following equation (4) (step S16). ).
K ′ = ((Rout−Rin) × PN) / PS (Formula 4)

Thereafter, similarly to the case where the change point pixel DE ₀ is a black pixel, the generated value Q is acquired based on the obtained randomization probability K ′ and the uniform random number. Then, only when the generated value Q = 1, the change point pixel DE ₀ is converted from a white pixel to a black pixel, and the converted pixel data is recorded in the memory 6 (steps S17 to S23).

Thus, when the gradation correction process for the change point pixel DE ₀ is completed, it is determined whether or not there is a change point pixel that has not been subjected to the gradation correction process (step S24). If there is a change point pixel that has not been subjected to gradation correction processing, the next change point pixel DE ₁ (see FIG. 5) is selected (step S25), and the selected change point pixel DE ₁ is the target. The processes of S3 to S23 are executed.

Thereafter, the processes of steps S3 to S23 are executed for other change point pixels until there is no change point pixel that has not been subjected to the gradation correction process. As a result, as shown in FIG. 8, the density measurement region CE setting process, the density measurement process, and the randomization correction process are executed for all change point pixels so as to follow the outer periphery of the target halftone dot H _0. At the same time, the same processing is executed for other halftone dots in the input binary image.

  In the gradation correction process as described above, since the pixel density is measured for all the change point pixels DE in the input binary image, the change that the halftone dot H has for one target halftone dot H is provided. The same pixel density value Rin as the number of point pixels is acquired. At this time, each density measurement region CE has the same size, and its position changes little by little along the outer periphery of the target halftone dot H. Therefore, as shown in FIG. 9, the change point pixel density Rin at the target halftone dot H has a normal distribution centered on the expected value (average density value Rav) of the change point pixel density Rin. Is the true density value of the target halftone dot H.

  On the other hand, the deviation ΔR between one point on the distribution curve and the average density value Rav becomes a density error with respect to the true density value, and there is also an error in the randomization probabilities K and K ′ obtained based on the change point pixel density Rin. Will occur. However, as described above, since the average value Rav of the change point pixel density Rin is the true density value of the target halftone dot H, the average value of the randomization probabilities K and K ′ is also the true density of the target halftone dot H. According to the value. Accordingly, even if the individual randomization probabilities K and K ′ vary, the randomization probabilities K and K ′ substantially following the true density value of the target dot H can be obtained as a whole. The density of the target halftone dot H can be corrected appropriately.

  As described above, according to the present embodiment, the size of the density measurement area CE is set to the size of the screen area of 50 lines / inch, and the density measurement area is smaller than the conventional gradation correction processing. Since the density is measured, it is possible to follow the edge portion of the density change existing in the input binary image, and it is possible to improve the clearness of the density information.

The size of the density measurement area CE is the same as that of the screen area of 50 lines / inch even if the number of screen lines actually used in the original image is higher than 60 lines / inch. Set to size. In this case, the density measurement region CE includes a plurality of halftone dots H, and the density of the halftone dots H is averaged. However, the paper size of the screen area of 50 lines / inch is as follows. Since it is extremely small as 0.508 mm × 0.508 mm≈0.258 mm ² and the human eye cannot identify the pattern information, there is no practical problem.

  Further, according to the present embodiment, the density measurement region CE is set with the change point pixel DE as the center, and the black of the surrounding halftone dot H in addition to the black pixel of the target halftone dot H in the density measurement region CE. Since the pixels are included, the density information of the surrounding halftone dots H can be reflected when the tone correction of the target halftone dot H is performed. Thereby, tone continuity between the halftone dots H in the image after tone correction can be improved, and the tone jump problem can be prevented.

  If the density measurement region CE includes a lot of density information of the surrounding halftone dots H, the continuity between the halftone dots H is improved, but the boundary between the halftone dots H is smoothed, and the tone correction is completed. The sharpness of the image will be lost. For this reason, it is desirable that the content of the density information of the surrounding halftone dots H is suppressed to a range in which the sharpness of the image and the continuity between the halftone dots H are well balanced. The length L ′ of one side is preferably 1 to 2 times the length L of one side of the screen area SE having the maximum number of screen lines (see FIG. 3B).

  Further, according to the present embodiment, density measurement is performed on all the change point pixels DE existing in the target halftone dot H, and a plurality of change point pixel densities Rin are obtained. Can be effectively prevented.

  That is, assuming that there is one change point pixel as a density measurement target and the positions of the change point pixels are common to all halftone dots, the tone correction error associated with the density measurement error is all halftone dots H on the image. It will be reflected in the same part. In this case, the gradation change at the boundary portion between adjacent halftone dots H becomes more conspicuous than the other portions, which causes moire.

  On the other hand, when the density measurement is uniformly performed for all the change point pixels DE existing in the target halftone dot H, the gradation correction error due to the density measurement error is not included in the halftone dot H. It is reflected in all change point pixels DE and appears dispersively within a large region. For this reason, the gradation change at the boundary portion between the adjacent halftone dots H does not stand out more than other portions, and it is possible to prevent the occurrence of moire.

  Further, according to the present embodiment, the change point pixel DE is changed from a black pixel to a white pixel, or from a white pixel to a black pixel by comparing the obtained randomization probabilities K and K ′ with a uniform random number. Since it is determined whether or not to change, it is possible to prevent the occurrence of bias in the position of the change point pixel DE whose value is changed. As a result, since the change point pixels DE whose values have been changed can be discretely distributed on the edge portion of the target halftone dot H, gradation adjustment can be performed without impairing the naturalness of the gradation. become.

  The embodiment of the present invention has been described above, but the present invention is not limited to the above-described configuration, and various modifications can be made within the scope of the invention described in the claims.

  For example, in the above-described embodiment, an example of reading AM screen plate making for newspaper printing has been described as an example, but the present invention can also be applied to the case of gradation correction of a binary image on an FM screen, Further, the present invention can be applied not only to newspaper printing but also to gradation correction of a photograph or the like.

  Further, in the above embodiment, the density measurement region CE is set for all the change point pixels DE existing at the edge of the halftone dot H, and the density measurement process and the gradation correction process are performed. Since it is sufficient if the point pixel density Rin is obtained, it is not always necessary to set the density measurement region CE for all the change point pixels DE.

  Furthermore, in the above-described embodiment, randomization correction processing is used for gradation correction of the change point pixel DE, but other correction processing such as an error diffusion method may be used.

  In the above embodiment, each time the change point pixel density Rin for one change point pixel DE is calculated, randomization correction is performed for the change point pixel DE. However, the pixel densities Rin of all the change point pixels DE are calculated. After that, randomization correction may be performed on each change point pixel DE.

It is a block diagram which shows one Embodiment of the gradation correction processing apparatus concerning this invention. It is explanatory drawing of a contour point pixel and a boundary point pixel. It is explanatory drawing of a density | concentration measurement area | region. It is a flowchart which shows the procedure of the gradation correction processing method concerning this invention. It is a figure which shows an example of an input binary image. It is a figure which shows an example of a gradation adjustment curve. It is a figure which shows an example which extracted only the contour point pixel from the input binary image. It is explanatory drawing of a density | concentration measurement area | region. It is a histogram which shows the state of the density distribution in an attention halftone dot. It is a figure explaining the setting method of the density | concentration measurement area | region in the conventional gradation correction process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Gradation correction processing apparatus 2 Change point detection part 3 Density measurement part 4 Randomization correction part 5 Random number generation part 6 Memory CE Density measurement area DE Change point pixel H Halftone dot Rin Change point pixel density Rout Target density SE Screen area

Claims (9)

A gradation correction processing method for correcting the gradation of an input binary image,
Detecting a change point pixel of the binary image;
Setting a density measurement region having a size corresponding to a screen region size of the maximum number of screen lines that can be used in the binary image with reference to the detected change point pixel;
Measuring the concentration in the set concentration measurement region;
Performing gradation correction using the measured density,
A gradation correction processing method, wherein a plurality of the density measurement regions are set along the outer periphery of a halftone dot.   The gradation correction processing method according to claim 1, wherein the density measurement region is centered on the change point pixel.   3. The gradation correction according to claim 1, wherein the length of one side of the density measurement region is 1 to 2 times the length of one side of the screen region of the maximum screen line number. Processing method.   4. The gradation correction processing method according to claim 1, wherein the density measurement region is set for each change point pixel of the halftone dot.   5. The gradation correction processing method according to claim 1, wherein the gradation correction is performed by converting a value of the change point pixel using a randomization correction process.   The randomization probability is obtained using a deviation between the measured density and the target density, and the randomization probability is compared with a uniform random number to determine whether or not to convert the value of the change point pixel. Item 6. The gradation correction processing method according to Item 5.   7. The gradation correction processing method according to claim 1, wherein the change point pixel is a contour point pixel and / or a boundary point pixel. A program for executing gradation correction processing for correcting the gradation of an input binary image,
A program for executing the gradation correction processing method according to claim 1. A gradation correction processing device that corrects the gradation of an input binary image,
Change point detecting means for detecting change point pixels of the binary image;
A density measurement area having a size corresponding to the screen area size of the maximum number of screen lines that can be used in the binary image on the basis of the change point pixel detected by the change point detection means on the outer periphery of the halftone dot. A plurality of measurement area setting means for setting along,
Density measuring means for measuring the density in the density measuring area set by the measuring area setting means;
A gradation correction processing apparatus comprising gradation correction means for performing gradation correction using the density measured by the density measurement means.

Images