JP4143232B2 - Image processing apparatus and image processing method - Google Patents

Description

[0001]
The present invention relates to an image processing apparatus and an image processing method.
[0002]
[Prior art]
The average density of the original image is obtained from the RGB signal read from the transparent original on which the image information is recorded, and the average density for each of R (red), G (green), and B (blue) is determined based on Evans' theorem. A method for adjusting the brightness and color balance of an output image by adjusting the density is known.
[0003]
[Problems to be solved by the invention]
However, the method of adjusting the average density of the original image to a predetermined density based on Evans' theorem assumes that the original image is substantially statistically free of color deviation. Therefore, when there are many pixels indicating a specific color component in the document image, the output image may cause a color cast. Also, when shooting a scene with a large difference in brightness between the main subject and the background, such as nighttime flash photography or backlighting, the main subject brightness is good when the average density of the entire image is referenced. May not be reproduced.
[0004]
The present invention has been made in view of the above-described background. For example, an input image such as a document image reproduces a good color even when the color (for example, hue, brightness, saturation) is uneven. The purpose is to do.
[0005]
[Means for Solving the Problems]
An image processing apparatus according to a first aspect of the present invention relates to an image processing apparatus, and the image processing apparatus includes a difference between a value of a target pixel in an input image and values of all other pixels excluding the target pixel. A calculation unit that calculates the difference, a counting unit that groups pixels whose difference calculated by the calculation unit is less than a predetermined value among all the other pixels, and counts the number of grouped pixels, and the counting unit. Generating means for generating a histogram of an image obtained by removing some or all of the grouped pixels from the pixels constituting the input image when the counting result exceeds a set number; and generating by the generating means A creating means for creating a gradation conversion curve based on the histogram, and a converting means for converting the gradation of the input image according to the gradation conversion curve.
[0006]
A second aspect of the present invention relates to an image processing apparatus, which calculates a difference between all pixel values in an input image and a pixel value of a predetermined color; Among all the other pixels, a pixel whose difference calculated by the calculation unit is less than a predetermined value is grouped, a counting unit that counts the number of grouped pixels, and a count number set by the counting unit And generating means for generating a histogram of an image obtained by removing some or all of the grouped pixels from pixels constituting the input image, and generating a histogram based on the histogram generated by the generating means. Creating means for creating a tone conversion curve; and converting means for converting the gradation of the input image according to the gradation conversion curve.
[0007]
According to a third aspect of the present invention, there is provided an image processing apparatus, wherein the image processing apparatus is configured for each of RGB colors among all the pixels in an input image in which each pixel is configured by RGB color signals. Pixels having a signal value difference equal to or greater than a first predetermined value are grouped as a first group, pixels having a second predetermined value or less are grouped as a second group, and the number of pixels in the first group and the second group A counting means for counting the number of pixels, and the input when the number of pixels in the first group and the number of pixels in the second group counted by the counting means exceed a set number, respectively. Generation means for generating a histogram of an image obtained by removing part or all of the first group of pixels and part or all of the second group of pixels from pixels constituting the image Comprises a generating means for generating gradation conversion curve based on the histogram generated by the generation unit, and a converting means for converting the gray level of the input image according to the gradation conversion curve.
[0008]
A fourth aspect of the present invention relates to an image processing method, and the image processing method calculates a difference between a value of a target pixel in an input image and values of all other pixels excluding the target pixel. And a counting step for grouping pixels whose difference calculated in the calculation step is less than a predetermined value among all the other pixels and counting the number of grouped pixels, and a counting result in the counting step is set. A generation step of generating a histogram of an image obtained by removing some or all of the grouped pixels from the pixels constituting the input image when the number exceeds the number, and based on the histogram generated in the generation step And a conversion step of converting the gradation of the input image according to the gradation conversion curve.
[0009]
A fifth aspect of the present invention relates to an image processing method, and the image processing method calculates a difference between the values of all pixels in the input image and the values of pixels of a predetermined color; A counting step for grouping pixels whose difference calculated in the calculation step is less than a predetermined value among all the other pixels and counting the number of the grouped pixels, and a number in which the counting result in the counting step is set A generation step of generating a histogram of an image obtained by removing some or all of the grouped pixels from the pixels constituting the input image, and a step based on the histogram generated in the generation step. The method includes a creation step of creating a tone conversion curve, and a conversion step of converting the gradation of the input image according to the gradation conversion curve.
[0010]
According to a sixth aspect of the present invention, there is provided an image processing method, wherein the image processing method is configured for each of RGB colors among all the pixels in an input image in which each pixel is composed of RGB color signals. Pixels having a signal value difference equal to or greater than a first predetermined value are grouped as a first group, pixels having a second predetermined value or less are grouped as a second group, and the number of pixels in the first group and the second group A counting step for counting the number of pixels, and the input when the number of pixels in the first group and the number of pixels in the second group counted in the counting step exceed a set number, respectively. Generating process for generating a histogram of an image obtained by removing a part or all of the first group of pixels and a part or all of the second group of pixels from pixels constituting the image If, comprising a creation step of creating a tone conversion curve based on the histogram generated in the generating step, a conversion step of converting the gradation of the input image according to the gradation conversion curve.
[0011]
In the image processing apparatus according to the first aspect of the present invention, for example, the counting unit preferably counts pixels having a color close to a preset color.
[0012]
In the image processing apparatus according to the first aspect of the present invention, for example, the input image is composed of RGB signals, and the counting means counts based on a difference in signal values of each color of RGB. It is preferable to determine the pixel to be.
[0013]
In the image processing apparatus according to the first aspect of the present invention, for example, the input image is composed of RGB signals, and the counting unit determines a pixel of interest based on a difference between RGB signal values. It is preferable to count pixels having a color close to the color of the target pixel.
[0014]
In the image processing device according to the first aspect of the present invention, for example, the counting means sets a pixel of saturation in the input image as a pixel of interest by setting a pixel whose difference between RGB signals exceeds a predetermined value as a target pixel. It is preferable to count high pixels.
[0015]
In the image processing device according to the first aspect of the present invention, for example, the counting means sets a pixel in which the difference between RGB signals is less than a predetermined value as a pixel of interest, and thereby saturation in the input image. It is preferable to count low pixels.
[0016]
In the image processing apparatus according to the first aspect of the present invention, for example, the counting means sets a pixel whose color difference of RGB signals exceeds a first reference value as a first target pixel, and is less than a second reference value. It is preferable to count pixels with high and low saturation in the input image by setting a certain pixel as the second pixel of interest.
[0017]
In the image processing apparatus according to the first aspect of the present invention, it is preferable that the image processing apparatus further includes, for example, an imaging unit that captures an image and uses the input image.
[0018]
In the image processing apparatus according to the first aspect of the present invention, for example, the imaging unit preferably includes an imaging element that reads an image formed on the imaging surface.
[0019]
In the image processing apparatus according to the first aspect of the present invention, for example, the imaging unit preferably further includes an optical system that forms an image on the imaging surface.
[0020]
In the image processing apparatus according to the first aspect of the present invention, it is preferable that, for example, in the imaging unit, the optical system forms an image of a transmission original on the imaging surface.
[0021]
In the image processing apparatus according to the first aspect of the present invention, for example, the transparent original is preferably a film.
[0022]
The image processing method according to the second aspect of the present invention includes a counting step of counting pixels having colors close to each other in an input image, and a histogram of pixel values constituting the input image according to a counting result in the counting step. And generating a histogram appropriately modified, and a processing step of processing the input image based on the histogram generated in the generation step.
[0023]
A memory medium according to a third aspect of the present invention is a memory medium storing an image processing program for controlling an image processing apparatus, and the image processing program counts pixels having colors close to each other in an input image. A generating step for generating a histogram in which a histogram of pixel values constituting the input image is appropriately modified according to a counting result in the counting step, and the input based on the histogram generated in the generating step. And a processing step of processing the image.
[0024]
DETAILED DESCRIPTION OF THE INVENTION
The present invention can be applied to an image processing apparatus such as a film scanner, a digital camera, or a computer. Hereinafter, an example in which the present invention is applied to a film scanner will be described as a preferred embodiment of the present invention.
[0025]
FIG. 1 is a diagram showing a configuration of a film scanner according to a preferred embodiment of the present invention. 101 is an illumination light source that illuminates the film, and 102 is a film folder that holds the film. For example, the film is held by sandwiching a non-image part (for example, perforation part) of the film from the front and back, and the direction of arrow A Driven by.
[0026]
Reference numeral 103 denotes an imaging lens (optical system) that forms an image on the imaging surface of a CCD linear image sensor (hereinafter simply referred to as CCD) 104 through light transmitted through a film (transmission original) irradiated by the illumination light source 101. An optical unit 105 includes an optical component group including an imaging lens 103 and a CCD 104 (for example, a mirror or the like may be included). The CCD 104 is arranged so that its longitudinal direction coincides with the Z direction. Due to this positional relationship, the main scanning direction which is the longitudinal direction of the CCD 104 and the sub-scanning direction which is the moving direction of the film folder 102 are perpendicular to each other. The optical unit 105 is driven in the lens optical axis direction (B direction) when performing focus adjustment.
[0027]
Reference numeral 106 denotes an analog image processing circuit that performs gain setting and clamping processing of the analog image signal output from the CCD 104, and reference numeral 107 denotes an A / D converter that converts the analog signal into a digital signal.
[0028]
Reference numeral 108 denotes a digital image processing circuit, which includes a histogram creation unit 109 that creates a histogram from a digital input signal, and a tone conversion curve that creates a tone conversion curve for converting the input signal by negative / positive conversion and tone conversion. And a creation unit 110. The digital image processing circuit 108 further includes a gamma conversion unit, various filter processing units, a CCD drive pulse generation unit, and the like. The digital image processing unit 108 can be configured by a gate array, for example.
[0029]
Reference numeral 111 denotes a line buffer for temporarily storing image data, and reference numeral 112 denotes an external interface unit for connection with an external device 113 such as a personal computer.
[0030]
Reference numeral 115 denotes a CPU as a system controller that controls the overall operation of the film scanner 100 based on predetermined software, and reference numeral 114 connects the CPU 115 to the image processing unit 108, the line buffer 111, and the interface unit 112. The CPU bus is composed of an address bus, a data bus, and a control bus.
[0031]
Reference numeral 121 denotes a sub-scanning motor for moving the film folder 102 in the sub-scanning direction (arrow A direction), for example, a stepping motor. Reference numeral 118 denotes a sub-scanning motor driver for driving the sub-scanning motor 121 in accordance with a command from the system controller 115.
[0032]
Reference numeral 116 denotes a focus motor for moving the optical unit 105 in the lens optical axis direction (arrow B direction), for example, a stepping motor. Reference numeral 117 denotes a focus motor driver for driving the focus motor 116 in accordance with a command from the system controller 115.
[0033]
In such a configuration, the film scanner 100 reads the film from the film scanner 100 to the external device 200 by communication between the software (firmware software) in the system controller 115 and software (driver software) for operating the film scanner from the external device 113. The image is transferred.
[0034]
For example, the invention applied to the film scanner 100 can be applied to a digital camera by changing the optical system unit 105 so that a subject image is formed on the imaging surface of the CCD 104. For example, the image processing unit 108 can be incorporated in a digital camera or a computer.
[0035]
Hereinafter, processing in the digital image processing unit 108 of the film scanner 100 will be described. FIG. 2 is a flowchart showing the flow of processing in the digital image processing unit 108. The digital image processing unit 108 includes, for example, a CPU and a nonvolatile memory, and is controlled by the CPU according to software installed in the memory from the external device 200, for example. This software can be provided to the external device 200 by being stored in, for example, a distributable memory medium (for example, a floppy disk, a CD-ROM, etc.).
[0036]
In step S <b> 201, the digital image processing unit 108 takes in the RGB signal for each pixel that has been A / D converted from the A / D conversion unit 107. In step S202, a histogram including the class and frequency of the signal value for each RGB color is created from the RGB signal value for each pixel. In step S203, the average value, the maximum value, and the minimum value of the signal values are calculated for each color of RGB based on the histogram for each color. Here, the minimum value may be the smallest signal value with a frequency of a predetermined ratio (for example, 1%) or more, and the maximum value may be the largest signal value with the frequency within a predetermined ratio (for example, 99%). Yes, but there is no limit to the rate of frequency.
[0037]
In step S204, a gain is calculated for each color of RGB using the average value, maximum value, and minimum value calculated in step S203. Here, the gain is an amplification factor when an input signal is amplified in order to reproduce an output image having an appropriate brightness (an output image having a predetermined density) based on Evans' theorem.
[0038]
In step S205, the second tone conversion curve for each color of RGB that is adapted to the original image (film image) by superimposing the gain for each color on the reference tone conversion curve set in advance for each color of RGB. Create In FIG. 3, 301 is an example of a reference gradation conversion curve, and 302 is an example of a second gradation conversion curve.
[0039]
In step S206, a negative / positive conversion process is performed on the input RGB signal using the second gradation conversion curve for each color created in step S205.
[0040]
In step S207, a histogram including the class and frequency of signal values is created from the RGB signals after negative / positive conversion. In addition, when reading the image of a positive film, the process of step S203-S207 is unnecessary, for example.
[0041]
In step S208, the maximum value and the minimum value of the signal value are calculated based on the histogram of the positive RGB signal by the same method as in step S203. In step S209, the shadow reference signal value and the highlight reference signal value are determined based on the maximum value and the minimum value calculated in step S208. In step S210, a third gradation conversion curve having a linear portion connecting the shadow reference signal value and the highlight reference signal value is created. 401 in FIG. 4 is an example of a third gradation conversion curve.
[0042]
In step S211, using the third tone conversion curve, tone conversion processing is performed on the positive RGB signal generated in step S206 and transferred to the external device 200 via the line buffer 111 and the interface 112. To do.
[0043]
FIG. 5 is a flowchart showing a specific example of the histogram creation processing in step S202.
[0044]
First, in step S501, a first histogram including a class of signal values and a frequency is generated for each color of RGB for all pixels constituting the document image. Here, the first histograms for R, G, and B are referred to as a first R histogram, a first G histogram, and a first B histogram, respectively. ,
In step S502, R, G, and B signal values TR, TG, and TB are stored with a pixel selected from a plurality of target image candidates in the document image as a target pixel. In step S503, the differences | TR-OR |, | TG-OG |, | TB-OB | of the RGB signal values OR, OG, OB of other pixels constituting the original image and TR, TG, TB are set to the respective colors. Calculate every time. Whether or not | TR-OR |, | TG-OG |, | TB-OB | are less than predetermined values V1R, V1G, and V1B, that is, | TR-OR | <V1R and | TG It is determined whether −OG | <V1G and | TB−OB | <V1B. If the determination result is YES, the process proceeds to step S505, and if the determination result is NO, the process proceeds to step S511. Here, the determination result is YES that the pixel value of the pixel is close to the pixel value of the target pixel, that is, the color (hue, lightness, saturation) of the pixel is close to the color of the target pixel. means.
[0045]
In step S505, an address of the pixel (that is, a pixel having a color close to the target pixel) is stored in association with the target pixel, thereby grouping pixels having a color close to the target pixel.
[0046]
In step S506, it is determined whether or not the processing in steps S503 to S505 has been executed for all the pixels. If the processing has been completed for all the pixels, the process proceeds to step S507. If unprocessed pixels remain, step S511 is performed. Then, the comparison pixel is switched to another unprocessed pixel, and steps S503 to S506 are executed.
[0047]
In step S507, the number of pixels whose color is close to that of the target pixel, that is, the number of pixels grouped in association with the target pixel is counted, and it is determined whether or not the count number is greater than a predetermined value (V2). Here, that the count value is larger than V2 means that there are more pixels whose color is close to that of the target pixel than V2, that is, there is a color deviation in the document image. On the other hand, a count value of V2 or less means that a pixel whose color is close to that of the target pixel is V2 or less, that is, the document image is not biased to a color close to the target pixel.
[0048]
If it is determined in step S507 that the count value is not greater than V2, that is, if it is determined that the document image is not biased to a color close to the target pixel, the above processing ends for all target pixel candidates in step S508. If there is any unprocessed target pixel candidate remaining, the process proceeds to step S512, and the processes of steps S502 to S507 and S511 are repeated with the unprocessed target pixel candidate as the target pixel.
[0049]
Here, all the pixels can be set as the target pixel candidates. However, in this case, since the calculation time is enormous, for example, it is preferably several to several hundreds so as to be approximately uniformly distributed over the entire surface of the original image. It is preferable to determine a target pixel candidate of about several degrees, more preferably about several tens. FIG. 6 is a diagram illustrating an example of a target pixel candidate.
[0050]
If it is determined in step S507 that the count value is greater than V2, that is, if it is determined that the document image is biased to a color close to the target pixel, the process proceeds to step S509. In step S509, a second histogram including the class and frequency of the signal values of the pixels grouped in association with the target pixel is created for each color of RGB. Here, the second histogram for R, G, and B is a second R histogram, a second G histogram, and a second B histogram, respectively. ,
In step S510, the first histogram created in step S501 is modified based on the second histogram created in step S507 to create a third histogram. Specifically, in this embodiment, for each class of signal values, the frequency of the third histogram is subtracted from the frequency of the first histogram by subtracting the value obtained by dividing the frequency of the second histogram by a predetermined number (V3). And
[0051]
More specifically, if the third histograms for R, G, and B are the 3R histogram, the 3G histogram, and the 3B histogram, respectively, the frequencies of the classes of the 3R histogram, the 3G histogram, and the 3B histogram are as follows: It is given by
[0052]
Frequency of third R histogram = frequency of first R histogram−frequency of second R histogram / V3
Frequency of 3G histogram = frequency of 1G histogram−frequency of 2G histogram / V3
Frequency of 3B histogram = frequency of 1B histogram−frequency of 2B histogram / V3
The third histogram created in this way is equivalent to a histogram of an image from which some or all of the pixels constituting the color (hue, lightness, saturation) in the original image are removed. Therefore, an average value, a maximum value, and a minimum value are calculated from the third histogram created in this way, and a gradation conversion curve (for example, in the case of a negative film, a second gradation conversion curve, a positive film) is calculated based on these values. In this case, a third gradation conversion curve) is created, and gradation conversion processing is executed according to this gradation conversion curve, so that it is possible to prevent a decrease in color reproducibility due to color deviation in the document image.
[0053]
Here, the value of V3 may be an arbitrary fixed value, or may be changed according to, for example, the number of grouped pixels.
[0054]
Further, the target pixel (target pixel candidate) may be determined according to the pixel value (signal value), for example, instead of being determined according to the position in the document image.
[0055]
Further, for example, a pixel having | TB-TG |, | TG-TB |, | TB-TR | of a predetermined value (V4) or more may be selected as the target pixel. It is possible to reduce the influence of the high color deviation portion on the color reproducibility.
[0056]
Further, as the target pixel, for example, a pixel having | TB-TG |, | TG-TB |, | TB-TR | may be a predetermined value (V5) or less may be selected. The influence of the low color saturation portion such as the background on the color reproducibility can be reduced.
[0057]
In the above embodiment, pixels having colors close to the pixel are grouped on the basis of the color (pixel value) of the pixel actually present in the document image (target pixel). Pixels having a color (pixel value) close to the color (pixel value) may be grouped.
[0058]
In the above embodiment, the second histogram is created for pixels belonging to one group grouped in association with one target pixel. However, the second histogram is created by referring to two or more groups. Also good. For example, a second histogram is created for pixels belonging to at least one of a plurality of groups. A more specific example is as follows.
[0059]
That is, | TB-TG |, | TG-TB |, | TB-TR | are grouped as pixels having a predetermined value (V4) or more as a first group, and | TB-TG |, | TG-TB | , | TB-TR | is grouped as a second group of pixels having a predetermined value (V5) or less, and a second histogram is created for the pixels belonging to the first group or the second group. As a result, both the influence of the highly saturated color deviation portion on the color reproducibility and the influence of the low saturation color deviation portion on the color reproducibility are reduced, and an output image having good color reproducibility is generated. be able to.
[0060]
The present invention may be applied to a system constituted by a plurality of devices (for example, a computer, an interface, a film scanner, etc.) or an apparatus (for example, a digital camera) composed of a single device. .
[0061]
Another object of the present invention is to supply a storage medium (or recording medium) in which a program code of software that realizes the functions of the above-described embodiments is recorded to a system or apparatus, and the computer (or CPU or CPU) of the system or apparatus. Needless to say, this can also be achieved by the MPU) reading and executing the program code stored in the storage medium. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the storage medium storing the program code constitutes the present invention. Further, by executing the program code read by the computer, not only the functions of the above-described embodiments are realized, but also an operating system (OS) running on the computer based on the instruction of the program code. It goes without saying that a case where the function of the above-described embodiment is realized by performing part or all of the actual processing and the processing is included.
[0062]
Furthermore, after the program code read from the storage medium is written into a memory provided in a function expansion card inserted into the computer or a function expansion unit connected to the computer, the function is determined based on the instruction of the program code. It goes without saying that the CPU or the like provided in the expansion card or the function expansion unit performs part or all of the actual processing and the functions of the above-described embodiments are realized by the processing.
[0063]
【The invention's effect】
According to the present invention, for example, even when an input image such as a document image has a color (for example, hue, brightness, saturation) deviation, a good color can be reproduced.
[Brief description of the drawings]
FIG. 1 is a diagram showing a configuration of a film scanner according to a preferred embodiment of the present invention.
FIG. 2 is a flowchart showing a flow of processing in the digital image processing unit.
FIG. 3 is a diagram illustrating an example of a reference gradation conversion curve and a second gradation conversion curve.
FIG. 4 is a diagram illustrating an example of a third gradation conversion curve.
FIG. 5 is a flowchart showing a specific example of histogram creation processing in step S202 of FIG.
FIG. 6 is a diagram illustrating an example of a target pixel candidate.

Claims (7)

Calculating means for calculating a difference between a value of a target pixel in the input image and values of all other pixels excluding the target pixel;
Counting means for grouping pixels whose difference calculated by the calculating means is less than a predetermined value among all the other pixels, and counting the number of grouped pixels ;
Generating means for generating a histogram of an image obtained by removing a part or all of the grouped pixels from the pixels constituting the input image when the counting result by the counting means exceeds a set number ;
Creating means for creating a gradation conversion curve based on the histogram generated by the generating means ;
An image processing apparatus comprising: conversion means for converting the gradation of the input image according to the gradation conversion curve . The input image is composed of RGB signals, the pixel of interest, the image processing apparatus according to claim 1, characterized in Rukoto is determined based on the difference in the respective color signal value of RGB. Calculation means for calculating a difference between the values of all pixels in the input image and the values of pixels of a predetermined color;
Counting means for grouping pixels whose difference calculated by the calculating means is less than a predetermined value among all the other pixels, and counting the number of grouped pixels;
Generating means for generating a histogram of an image obtained by removing a part or all of the grouped pixels from pixels constituting the input image when the counting result by the counting means exceeds a set number;
Creating means for creating a gradation conversion curve based on the histogram generated by the generating means;
An image processing apparatus comprising: conversion means for converting the gradation of the input image according to the gradation conversion curve. Grouping, as a first group, pixels in which the difference in signal value of each RGB color is greater than or equal to a first predetermined value among all the pixels in the input image in which each pixel is composed of RGB color signals, Counting means for grouping pixels having a second predetermined value or less as a second group and counting the number of pixels of the first group and the number of pixels of the second group;
When the number of pixels of the first group and the number of pixels of the second group counted by the counting unit exceed a set number, the pixels of the input image Generating means for generating a histogram of an image from which part or all of the pixels and part or all of the pixels of the second group are removed;
Creating means for creating a gradation conversion curve based on the histogram generated by the generating means;
An image processing apparatus comprising: conversion means for converting the gradation of the input image according to the gradation conversion curve. A calculation step of calculating a difference between the value of the target pixel in the input image and the values of all other pixels excluding the target pixel;
A counting step of grouping pixels whose difference calculated in the calculation step is less than a predetermined value among all the other pixels and counting the number of grouped pixels;
A generating step of generating a histogram of an image obtained by removing a part or all of the grouped pixels from the pixels constituting the input image when the counting result in the counting step exceeds a set number;
A creation step of creating a gradation conversion curve based on the histogram generated in the generation step;
A conversion step of converting the gradation of the input image according to the gradation conversion curve. A calculation step of calculating a difference between values of all pixels in the input image and values of pixels of a predetermined color;
A counting step of grouping pixels whose difference calculated in the calculation step is less than a predetermined value among all the other pixels and counting the number of grouped pixels;
A generating step of generating a histogram of an image obtained by removing a part or all of the grouped pixels from the pixels constituting the input image when the counting result in the counting step exceeds a set number;
A creation step of creating a gradation conversion curve based on the histogram generated in the generation step;
A conversion step of converting the gradation of the input image according to the gradation conversion curve. Grouping, as a first group, pixels in which the difference in signal value of each RGB color is greater than or equal to a first predetermined value among all the pixels in the input image in which each pixel is composed of RGB color signals, A counting step of grouping pixels having a second predetermined value or less as a second group, and counting the number of pixels of the first group and the number of pixels of the second group, respectively;
When the number of pixels of the first group and the number of pixels of the second group counted in the counting step exceed a set number, the pixels of the input image Generating a histogram of an image in which part or all of the pixels and part or all of the pixels of the second group are removed;
A creation step of creating a gradation conversion curve based on the histogram generated in the generation step;
A conversion step of converting the gradation of the input image according to the gradation conversion curve.

Images