JP4883030B2 - Image processing apparatus, image processing method, and image processing program - Google Patents

Description

  The present invention relates to an image processing apparatus, an image processing method, and an image processing program that perform binarization on image data captured with multiple values such as a camera captured image.

  In the case of recognizing an image shot by a shooting system such as a still camera or a video camera, it is possible to improve the accuracy of the recognition rate by correcting the image quality of the input image according to the shooting situation. In particular, when a character image written on a business card or a whiteboard or a character image captured by a scanner is recognized, binarization processing is often used as an image quality correction method.

The image binarization process is a process of determining one of two levels, each pixel being white or black, based on a certain threshold value. So far, (1) fixed threshold method, (2) differential histogram method (Non-Patent Document 1), (3) discriminant analysis method (Non-Patent Document 2), etc. have been proposed as binarization processing. A method applying the above has also been proposed. For example, a method for correcting image quality degradation (Patent Documents 1 to 3), a method for determining a threshold value based on a density histogram (Patent Document 4), a threshold value obtained for a partial region of an image based on a surrounding threshold value (Patent Document 5) and the like have been proposed. Further, methods for calculating the correction amount are also disclosed in Patent Documents 6 and 7.
JP 2006-195622 A JP 2002-6474 A JP 2001-245148 A JP 2004-180000 A Japanese Patent Publication No. 6-14374 JP 2001-060266 A JP 2007-025935 A Yukiichi Sakai, “Basics and Applications of Digital Image Processing”, CQ Publishing, August 2003, p47 Nobuyuki Otsu, “Automatic threshold selection method based on discriminant and least squares criterion”, IEICE Transactions, vol. J63-D, no4, pp. 349-356, 1980

  However, in the conventional technology as described above, it is necessary to perform binarization processing after performing processing (noise removal, blur removal, etc.) for correcting the image quality degradation of the photographed image, and image quality degradation for the binarization processing. It is necessary to check whether the correction result is optimal. Therefore, the conventional technique has a problem that it takes time to adjust the correction parameter for image quality degradation before binarization.

  The present invention has been made in view of such problems, and it is not necessary to confirm whether the correction result by the image quality deterioration correction is optimal for the binarization processing, and the deterioration correction and binarization can be performed with high accuracy. It is an object of the present invention to provide an image processing apparatus that can perform the processing.

  An aspect of the image processing apparatus according to the present invention corrects a correction amount for binarization processing for binarizing the input image and deterioration from the original image to be captured based on the input image. An image quality correction amount calculation unit that calculates a correction amount by integrating correction amounts for image quality degradation, an estimation image generation unit that generates an estimation image obtained by estimating an original image to be imaged based on the correction amount; It is provided with.

  According to one aspect of the image processing apparatus of the present invention, a correction amount obtained by integrating a correction amount for binarization and a correction amount for image deterioration is obtained, and an estimated image is created based on the correction amount. It is not necessary to confirm whether the image quality deterioration correction result is optimal for the binarization process, and deterioration correction and binarization can be performed with high accuracy.

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.
[First Embodiment]
FIG. 1 is a functional block diagram showing a schematic configuration example of an image processing apparatus 10 according to the first embodiment of the present invention. This image processing apparatus 10 is based on an input image, a correction amount for binarization processing for binarizing the input image and a correction for image quality deterioration for correcting deterioration from the original image to be photographed. An image quality correction amount calculation unit 121 that calculates the correction amount by integrating the amounts and an estimated image generation unit 122 that generates an estimated image obtained by estimating the original image to be photographed based on the correction amount are provided. Here, the original image means an image acquired when a shooting target is shot in an environment where image quality degradation cannot occur. For example, when a monochrome binary image is captured, the original image of the captured image is composed of white and black.

  When a subject is photographed in a real environment, camera lens distortion, dark current noise, defocusing, and other deterioration occur. Therefore, a gray image that is an intermediate color is captured instead of a complete black and white image. An estimated image means an image generated when processing is performed from a captured image captured in such a real environment to bring it closer to the original image while reducing image quality degradation. When the original image can be completely approximated, the estimated image and the original image are equal.

  FIG. 2 is a functional block diagram illustrating a more detailed configuration example of the image processing apparatus 10. As illustrated in FIG. 2, the image processing apparatus 10 includes an image input unit 110, a data processing unit 120, a data storage unit 130, and an image output unit 140. The data processing unit 120 includes an estimated image evaluation unit 123 and a parameter update unit 124 in addition to the image quality correction amount calculation unit 121 and the estimated image generation unit 122 described above.

  The image input unit 110 has a function of inputting an image captured by an imaging system such as a still camera or a video camera and passing the image to the image quality correction amount calculation unit 121. The image quality correction amount calculation unit 121 has a function of calculating the image quality correction amount based on the maximum a posteriori probability estimation framework using the observation model of the captured image and the prior knowledge stored in the prior knowledge function storage unit 132. ing. Here, the prior knowledge is a characteristic of a captured image that is known in advance, and is information such as, for example, a binary image.

  The estimated image generation unit 122 has a function of generating an estimated image using the image quality correction amount calculated by the image quality correction amount calculation unit 121. The estimated image evaluation unit 123 has a function of evaluating whether or not the estimated image generated by the estimated image generation unit 122 has approached the original image. The parameter update unit 124 updates various parameters such as the parameters used when calculating the image quality correction amount, the function parameters used when generating the estimated image, and the convergence parameter, and used until the previous correction process. Each parameter has a function of storing in the parameter storage unit 131. For example, the parameter update unit 124 updates the image quality correction coefficient λ of the image quality correction amount calculated by the image quality correction amount calculation unit 121 and the parameter of the degradation function h used by the estimated image generation unit 122, and the parameter storage unit 131. To store.

Since the image quality correction process generates an estimated image through an iterative process, parameters used in the correction coefficient λ _n and the degradation function h _n (i, j) may be updated according to the number of iterations. . The image output unit 140 outputs the generated estimated image y (i, j) to a display device (not shown) such as a display when the estimated image evaluation unit 123 determines that the image quality correction processing has been completed. To do.

  The data storage unit 130 includes a parameter storage unit 131 and a prior knowledge function storage unit 132. The parameter storage unit 131 stores variable parameters and convergence parameters of functions used for generating an estimated image. The prior knowledge function storage unit 132 stores functions such as an image quality correction coefficient λ and a deterioration function h used by the image quality correction amount calculation unit 121.

  Next, a calculation method when the image quality correction amount calculation unit 121 calculates the image quality correction amount will be described. In general, an observation model of an image, that is, a captured image g (i, j) that is an input image can be expressed by Equation 1.

Here, h (i, j) represents a degradation function, f (i, j) represents an original image, and n (i, j) represents a noise component. The degradation function h (i, j) is a function that represents degradation that occurs during image capture. For example, the deterioration function h (i, j) includes a blur function that represents out of focus.

An image f _map having the maximum posterior probability that the estimated image calculated based on the photographed image g (i, j) matches the original image f (i, j) is expressed by Equation 2.

When Pr (f (i, j) | g (i, j)) is expressed by log and Bayes' theorem is used, Expression 2 is expressed as Expression 3.

In Expression 3, when the occurrence probability distribution of the noise component n (i, j) follows a Gaussian distribution, Pr (g (i, j) | f (i, j)) can be expressed as Expression 4.

Further, from Expression 3, f _map can be expressed as Expression 5A.

Here, obtaining the maximum value max of the quadratic function for f (i, j) in Equation 5A is equivalent to obtaining the minimum value min of the quadratic function in Equation 5B.

When the correction amount calculated by the image quality correction amount calculation unit 121 is L (i, j), the correction amount L (i, j) can be expressed by Expression 6.

Pr (f (i, j)) represents the prior probability distribution of the binary image. This prior probability distribution Pr (f (i, j)) is set as prior knowledge function b (i, j). The prior knowledge function b (i, j) is stored in the prior knowledge function storage unit 132. If the estimated image is y (x, y), Expression 6 can be expressed as Expression 7.

It can be said that the estimated image y (i, j) has estimated the original image f (i, j) by obtaining y (i, j) that minimizes Equation 7. Since Equation 7 is a quadratic function, solutions such as the steepest descent method and the conjugate gradient method are known for minimizing the quadratic function. For example, when the minimum value y (i, j) is obtained using the steepest descent method, it can be solved as Equation 8.

Here, n represents the number of repetitions, and λ _n represents an image quality correction amount coefficient. L ′ _n (i, j) means a differentiation of Ln (i, j) and is expressed by Equation 9. Further, b ′ _n (i, j) means the differentiation of the prior knowledge function bn (i, j). The image quality correction amount calculation unit 121 calculates the image quality correction amount λ _n L ′ _n (i, j) using the second term on the right side of Equation 8 as the image quality correction amount.

The estimated image generation unit 122 and the image quality correction amount λ _n−1 L ′ _n−1 (i, j) calculated by the image quality correction amount calculation unit 121 and the n−1th estimated image y _n−1 (i, j) ) Is used to generate the n-th estimated image y _n (i, j) using Equation (8). Note that n represents the number of iterations, and the image quality correction coefficient λ _n is stored in the parameter storage unit 131 as a different coefficient for each processing number.

The image quality correction amount calculation unit 121 calculates the image quality correction amount λ _n L ′ _n (i, j) using Equation 9. The prior knowledge function b _n (i, j) of the binary image defined by Equation 7 is defined as follows.

Since it is assumed that the photographed image is composed of binary values, there is a slight deterioration in image quality during photographing, but when a histogram is created, a bimodal histogram is obtained. The peak portions of the histogram are set as binary representative values α and β (FIG. 3). For b _n (i, j), which one of formulas 10A and 10B is adopted is determined in consideration of the relationship between the pixel value I _n (i, j) and surrounding pixel values.

For the pixel value I _n (i, j), a threshold value is provided to determine whether to use the formula 10A or the formula 10B. If the pixel value I _n (i, j) is smaller than a preset threshold value, the expression 10A is adopted, and if the pixel value I _n (i, j) is larger than the threshold value, the expression 10B is adopted. For example, the threshold value can be set as (α + β) / 2 using α and β which are representative values. Here, _assuming that y _n (i, j) is a pixel value 90 and binary representative values α: 36 and β: 240, ((α + β) / 2)) = 138 is set as the threshold value. (FIG. 4). y _n (i, j) pixel values 90 is smaller than the threshold _{138, y n (i, j} ) is the formula 10A can be adopted. Instead of using the value of (α + β) / 2 as a comparison target, a fixed threshold value stored in the parameter storage unit 131 may be used.

By substituting Equations 10A and B into Equation 9 to calculate L ′ _n (i, j), Equations 11A and 11B are obtained.

Note that which one of the formula 11A and the formula 11B is adopted is in accordance with the b _n (i, j) determining means. An image quality correction amount λ _n L ′ _n (i, j) is calculated using L ′ _n (i, j) and the image quality correction coefficient λ _n stored in the parameter storage unit 131. L ′ _n (i, j) includes a first term used as a correction amount for image quality deterioration and a second term b ′ _n (i, j) used as a correction amount for binarization. Yes. That is, the image quality correction amount λ _n L ′ _n (i, j) is integrated with the image quality deterioration correction amount and the binarization correction amount.

As described above, the estimated image generation unit 122 and the image quality correction amount λ _n−1 L ′ _n−1 (i, j) calculated by the image quality correction amount calculation unit 121 and the n− _1th estimated image y _{n− 1} (i, j) is used to generate the _nth estimated image y _n (i, j) using Equation 8. The estimated image y _n (i, j) generated in this way is evaluated by the estimated image evaluation unit 123 to evaluate how close to the original image f (i, j). The estimated image evaluation unit 123 performs evaluation based on the image observation model (Equation 1). An estimated image y _n (i, j) obtained by estimating the original image f (i, j) is expressed by the following Expression 12 using Expression 1.

By transforming Equation 12, Equation 12 is obtained.

Here, it can be evaluated that the original image f (i, j) and the estimated image y (i, j) are approximated as the value of the expression on the left side of Expression 13 is smaller. Therefore, the left side is set as Expression 14.

If the evaluation value E is small, it is determined that the estimated image y (i, j) is close to the original image f (i, j). If the evaluation value E is large, the generated estimated image y (i, j) is the original image. It is determined that the image f (i, j) is not sufficiently similar. The estimated image evaluation unit 123 approximates the estimated image y (i, j) to the original image to be imaged by determining whether or not the evaluation value E is equal to or greater than a preset threshold value E _val. Determine whether or not. Note that when the estimated image evaluation unit 123 determines that the estimated image y (i, j) has sufficiently approached the original image f (i, j), the image quality correction process is terminated. When the estimated image evaluation unit 123 determines that the estimated image y (i, j) is not sufficiently approximated with respect to the original image f (i, j), the image processing apparatus 10 performs image quality correction. The amount correction unit 121 calculates the image correction amount again, and generates an estimated image y (i, j) based on the image correction amount.

Next, a processing procedure for the operation of the image processing apparatus configured as described above will be described. FIG. 5 is a flowchart showing a processing procedure of the image processing apparatus 10 according to the first embodiment of the present invention. First, a shooting target is shot with a shooting device such as a still camera or a video camera (step S10). The image input unit 110 inputs the captured image and outputs it to the data processing unit 120. The image quality correction amount calculation unit 121 sets b _n (i, j) as a correction amount for binarization in consideration of the relevance of the pixel value I _n (i, j) and surrounding pixel values ( Step S11). In the present embodiment, b _n (i, j) is stored in advance in the prior knowledge function storage unit 132 as a prior knowledge function, so the image quality correction amount calculation unit 121 is based on the acquired captured image. The optimum b _n (i, j) is selected. For example, when the pixel value I _n (i, j) is close to α, the expression 10A is selected, and when it is close to β, the expression 10B is selected.

Further, the image quality correction amount calculation unit 121 uses L ′ _n (i, j) and the image quality correction coefficient λ _n stored in the parameter storage unit 131 with respect to the expression 11A or 11B, thereby performing binarization. An image quality correction amount λ _n L ′ _n (i, j) in which the correction amount and the deterioration correction amount are integrated is calculated (step S12). Here, if the number of times of generating the estimated image using the image quality correction amount is 1 (Yes in Step S13), the estimated image generating unit 122 performs noise removal and image sharpening processing (Step S14). . When the number of times of processing is the first time, it is assumed that the captured image g (i, j), which is the estimated image y ₀ (i, j) of the 0th time, has undergone image quality degradation due to noise or blur, and is set in advance. Based on the set parameters, noise removal and sharpening processing are performed on y ₀ (i, j). On the other hand, if the number of processes for generating the estimated image using the image quality correction amount is the second or later (No in step S13), the estimated image generation unit 122 generates the image quality correction amount λ generated by the image quality correction amount calculation unit 121. _{Using n} L ′ _n (i, j) and the estimated image y _n−1 (i, j) generated at the ( _n−1 ) th time, the nth estimated image y _n (i, j) is _expressed by Equation 8. Generate (step S15).

The estimated image evaluation unit 123 generates an evaluation value E by Expression 14 and evaluates the estimated image (step S16). The estimated image evaluation unit 123 determines whether or not the calculated evaluation value E is greater than or equal to a preset threshold value E _val so that the estimated image y (i, j) is an original image to be captured. It is determined whether or not (step S17). Here, if it is within the threshold value _{E val} evaluation value E (Yes in step S17), the estimated image is completed the image correction processing as approximates well the original image (END). If the evaluation value E is not within the threshold value E _val (No in step S17), it is determined that the generated estimated image is not close to the original image, and the parameter update unit 124 calculates the correction amount. The parameter to be used is updated (step S18).

Specifically, the parameter update unit 124 updates the parameters of the degradation function h _n (i, j) and the parameters such as the image quality correction coefficient λ _n and the binary correction coefficient γ _n . Then, the process returns to step S11, and the correction amount is calculated again. For example, when image quality degradation caused by shooting is blurred, the image quality degradation is corrected each time the estimation process is performed, and the size of the blur is reduced. Therefore, the parameter of the degradation function hn (i, j) is reduced according to the number of processing times. A typical deterioration function h _n (i, j) expressing blur can be expressed by a two-dimensional Gaussian function (Formula 15). In this case, the parameter σn represents the magnitude of blur, and the value of σn is decreased according to the number of processing times. Further, the accuracy of the image quality correction coefficient λn is also improved by updating the coefficient according to the number of processes.

Here, the binary correction coefficient γ _n is a weighting coefficient for the expressions 10A and 10B that are binary correction amounts. If the binary correction coefficient γ _n is increased, the convergence time is shortened, but the accuracy is lowered. On the other hand, if it is made smaller, the convergence time becomes longer, but the accuracy is improved. The binary correction coefficient γ _n may be determined according to the system requirements to which the process is applied.

  In the image processing apparatus 10 configured as described above, the correction amount considering the prior probability distribution of the binary image and the correction amount considering the image quality deterioration at the time of photographing are integrated and obtained by the image quality correction amount calculation unit 121. The binarization process can be performed while correcting the image. That is, it is not necessary to adjust in advance the image quality deterioration correction processing parameters for the binarization process before the binarization process.

  6 to 8 are diagrams illustrating processing results by the image processing apparatus according to the present embodiment. FIG. 6 is a photographed image to be photographed. FIG. 7 is a diagram showing a binarization processing result image using a discriminant analysis method after visual image quality deterioration correction is performed. FIG. 8 is a binarization processing result image obtained by performing image quality correction processing by the image processing apparatus according to the present embodiment. As described above, the image (FIG. 8) that has been subjected to the image quality correction processing by the image processing apparatus according to the present embodiment is sharper in FIG. 6 than the image that has been subjected to visual image quality deterioration correction shown in FIG. It can be seen that the correction processing has been performed on the displayed captured image.

  As described above, according to the image processing apparatus of the present embodiment, it is possible for the user to perform the optimum binarization process without the need for trial and error in advance for the parameters of the image quality deterioration correction process.

In the above description, it is determined whether to use Expression 11A or Expression 11B according to the pixel value I _n (i, j). However, b _n (i, j ) May be determined. For example, the pixel values of the eight surrounding neighboring pixel values I _{n (i,} j) is smaller than the pixel value I _{n (i,} j), if the binary representative value for the pixel values of the neighboring surroundings 8 alpha, pixel value A binary representative value of I _n (i, j) is set to β. That is, a method may be used in which the pixel value I _n (i, j) is affected by surrounding pixel values due to image quality degradation during shooting.

[Second Embodiment]
FIG. 9 is a functional block diagram of an image processing apparatus according to the second embodiment of the present invention. In addition, about the component substantially the same as 1st Embodiment, the description is abbreviate | omitted by attaching | subjecting the same code | symbol. The feature of the second embodiment is that the data processing unit 120A is further provided with a plane decomposition unit 151 and a plane synthesis unit 152. The data processing unit 120A includes a plane decomposing unit 151 and a plane combining unit 152, and the plane decomposing unit 151 has a function of decomposing a captured image into color planes. The plane decomposition unit 151 can decompose the input image into R plane, G plane, and B plane when the input image is composed of RGB, and converts it into Y plane, U plane, and V plane when composed of YUV. It is possible to disassemble.

The plane synthesis unit 152 has a function of synthesizing the separated color planes. Plane synthesizing unit 152, when the evaluation value E of the estimated images in all color planes were within threshold E _val, to synthesize the color planes.

  9 have the same function as each of the components 121 to 124 shown in FIG. 2, but the image quality correction process is performed for each color plane separated by the plane separation unit 151. Can be done.

  Next, an image correction process of the image processing apparatus 11 configured as described above will be described. FIG. 10 is a flowchart of the processing method of the image processing apparatus 11 according to the second embodiment of the present invention.

Steps S10 to S18 in FIG. 9 are the same as steps S10 to S18 in FIG. In the second embodiment, after shooting the shooting target in step S10, the shot image is decomposed into a plurality of color planes by the plane decomposition unit 151 (step S100). Then, a binary correction amount and an image quality correction amount are calculated for each separated color plane (steps S11 and S12), and processing according to the number of processing is performed (steps S13 to S15). Further, the estimated image evaluation unit 123 performs estimated image evaluation for each color plane (step S16). Then, if the evaluation value E is within the threshold value _{E val} (Yes in step S17), synthesized color planes (step S101) and ends the image quality correction process. On the other hand, if evaluation value E is larger than threshold value Eval (No in step S17), parameters are updated by parameter updating unit 124 (step S18), and the process returns to step 11.

  The image processing apparatus 11 according to the second embodiment has the same effects as those of the first embodiment, and further has the following effects. In other words, in the image processing apparatus 11, the plane decomposing unit 151 decomposes the captured image into, for example, three RGB planes, obtains correction amounts for each color plane, performs image quality correction processing, and decomposes the brain combining unit. A color image is generated by combining the planes. Therefore, the image processing apparatus 11 can binarize with high accuracy even for an object composed of color binary components such as a road sign and a signboard. For example, the “parking prohibition” sign is composed of red and blue, and the captured image can be accurately separated into binary values of red and blue.

  As described above, according to the present embodiment, a captured image is decomposed into color planes, and image correction processing is performed on each color plane, so that image quality correction processing can be performed with high accuracy even for color images. it can.

  It should be noted that the present invention is not limited to the above-described embodiment, and it is needless to say that various modifications can be made without departing from the gist of the present invention.

1 is a functional block diagram of an image processing apparatus according to a first embodiment of the present invention. FIG. 2 is a functional block diagram illustrating a more detailed configuration example of the image processing apparatus according to the first embodiment of the present invention. It is a figure which shows the histogram of a picked-up image. It is a figure which shows the relationship between a histogram and binary representative value (alpha), (beta). 3 is a flowchart illustrating image quality correction processing of the image processing apparatus according to the first embodiment of the present invention. It is a captured image. It is an image that has been subjected to visual quality degradation correction. It is an image on which image quality correction processing has been performed by the image processing apparatus according to the present embodiment. It is a functional block diagram of the image processing apparatus which concerns on the 2nd Embodiment of this invention. It is a flowchart of the image quality correction process of the image processing apparatus which concerns on the 2nd Embodiment of this invention.

Explanation of symbols

10, 11 Image processing apparatus 110 Image input unit 120, 120A Data processing unit 121 Image quality correction amount calculation unit 122 Estimated image generation unit 123 Estimated image evaluation unit 124 Parameter update unit 130 Data storage unit 131 Parameter storage unit 132 Prior knowledge function storage unit 140 Image output unit 151 Plane decomposition unit 152 Plane synthesis unit b Prior knowledge function E Evaluation value E _val threshold f Original image f _map image g Captured image h Deterioration function I _n Pixel value n Noise component Pr Prior probability distribution y Estimated image α, β representative value γ _n binary correction coefficient λ _n image quality correction coefficient λ _n L image quality correction amount σ _n parameter

Claims (17)

Based on the input image, a correction amount by integrating a binarization correction amount for binarizing the input image and a correction amount for image quality deterioration for correcting deterioration from the original image to be photographed An image quality correction amount calculation unit for calculating
An image processing apparatus comprising: an estimated image generation unit configured to generate an estimated image obtained by estimating an original image to be imaged based on the correction amount. The image processing apparatus according to claim 1, further comprising an estimated image evaluation unit that evaluates whether or not the estimated image estimated by the estimated image generation unit approximates the original image to be captured. The image processing apparatus further includes a prior knowledge function storage unit that stores a correction function in accordance with characteristics of the imaging target that are known in advance, and the image quality correction calculation unit uses the correction function stored in the prior knowledge function storage unit. The image processing apparatus according to claim 1, wherein the correction amount for binarization is calculated. The image quality correction amount calculation unit repeatedly calculates the correction amount until the estimated image evaluation unit determines that the estimated image estimated is sufficiently close to the original image to be captured,
The estimated image generation unit generates an estimated image estimated for the nth time based on the estimated image estimated for the (n-1) th time and the correction amount calculated by the image quality correction amount calculation unit for the nth time. Or the image processing apparatus of 3. The estimated image evaluation unit calculates an evaluation value for evaluating whether or not the estimated image approximates the original image to be captured, and compares the evaluation value with a preset threshold value, The image processing apparatus according to claim 2, wherein it is determined whether or not the estimated image approximates the original image to be captured. When the estimated image evaluation unit determines that the estimated image is not approximate to the original image to be captured, the estimated image generation unit is configured to perform the n-1th estimation of the estimated image and the image quality correction. The image processing apparatus according to claim 4 or 5, wherein an estimated image to be estimated for the nth time is generated based on the correction amount calculated by the amount calculating unit for the nth time. A parameter storage unit for storing parameters used when calculating the correction amount previously;
The said correction amount calculation part acquires the said parameter from the said parameter memory | storage part, and sets the parameter used when calculating the said correction amount to an optimal value in any one of Claims 1 thru | or 6. Image processing device. A plane separation unit that separates the input image into a plurality of color planes;
The image processing apparatus according to claim 1, wherein the estimated image generation unit generates the estimated image for each color plane decomposed by the plane decomposition unit. Based on the input image, a correction amount by integrating a binarization correction amount for binarizing the input image and a correction amount for image quality deterioration for correcting deterioration from the original image to be photographed To calculate
An image processing method for generating an estimated image obtained by estimating an original image to be imaged based on the correction amount. The image processing method according to claim 9, wherein the estimated image is evaluated whether or not the estimated image approximates the original image to be photographed. A correction function is stored in accordance with the characteristics of the shooting target that are known in advance,
The image processing method according to claim 9 or 10, wherein the correction amount for binarization is calculated using the stored correction function. Repeatedly calculating the correction amount until it is determined that the estimated image is sufficiently close to the original image to be imaged,
12. The image processing method according to claim 9, wherein an estimated image estimated at the nth time is generated based on the estimated image estimated at the (n−1) th time and the correction amount calculated at the nth time. Calculating an evaluation value for evaluating whether or not the estimated image approximates the original image to be imaged;
13. The method according to claim 9, wherein it is determined whether or not the estimated image approximates the original image to be captured by comparing the evaluation value with a preset threshold value. Image processing method. When it is determined that the estimated image is not approximate to the original image to be captured, the estimated image estimated at the (n-1) th time and the correction amount calculated by the image quality correction amount calculating unit at the nth time. The image processing method according to claim 12 or 13, wherein an estimated image to be estimated for the nth time is generated based on the estimated image. Stores the parameters used in the previous calculation of the correction amount,
15. The image processing method according to claim 9, wherein a parameter used when calculating the correction amount is set to an optimum value using the stored parameter. Separating the input image into a plurality of color planes;
The image processing method according to claim 9, wherein the estimated image is generated for each of the decomposed color planes.   An image processing program for executing the image processing according to any one of claims 9 to 16.