Color image enhancement method and system for people with abnormal red and green color senses based on visual perception characteristics

Abstract

The invention belongs to the technical field of image processing, and particularly relates to a color image enhancement method and system for people with abnormal red and green color senses based on visual perception characteristics. The enhancement method comprises the following steps: judging an image enhancement type based on a red-green green vision anomaly type; the types of the red and green vision abnormality include weak green, blind green, severe weak red, blind red and weak common red; the image enhancement type comprises a first enhancement type, a second enhancement type and a third enhancement type; generating an abnormal cone spectrum sensitive curve based on the image enhancement type; measuring the three-primary-color spectral radiation distribution of the image display device; obtaining a conversion matrix from an image RGB value to an LMS value based on the abnormal cone spectrum sensitive curve and the three-primary-color spectrum radiation distribution; the RGB value of the enhanced image is obtained based on the conversion matrix, the accuracy degree of the color weakness level is not needed, and a uniform enhancement algorithm can be used for people with green blindness and people with green weakness to realize color image enhancement.

Description

Technical Field

[0001] The invention belongs to the technical field of image processing, and in particular relates to a color image enhancement method and system for red-green abnormal color vision persons based on visual perception characteristics. Background Art

[0002] People with red and green color vision abnormalities have a reduced ability to discern colors, which affects their ability to obtain information from images and videos. The quality of life of these people can be greatly improved through color image and video enhancement. Among people with color vision abnormalities, the number of people with red and green color weakness accounts for about 75% of the total number of people with color vision abnormalities, which is much larger than the proportion of people with red and green color blindness.

[0003] The existing technology needs to design different color image enhancement algorithms for red-green color blindness and red-green color deficiency. The color image enhancement methods for red-green color blindness mainly include: custom difference sum, hue rotation and optimization methods. The color image enhancement methods for red-green color deficiency mainly include: analyzing the color perception difference between normal color vision and red-green color deficiency from the chromaticity ratio and hue difference, establishing a color shift model in the chromaticity space to control the degree of color difference to achieve image enhancement; enhancing the color perception of red-green color deficiency in the color space, retaining the naturalness of the enhanced image; image enhancement based on the content of brightness and chromaticity information. The above methods are mainly based on the color blindness confusion line in the CIE chromaticity diagram as a theoretical basis, and various algorithms in the field of image processing are used to achieve the purpose of enhancement. They do not start from the visual perception characteristics of red-green abnormal color vision people for color images. Related studies have shown that red-green abnormal color vision people have their own particularity in distinguishing the colors of objects in color images. They are affected by the distribution of color information of objects in color images, and their color discrimination ability is not as low as predicted by the color blindness confusion line.

[0004] Related studies have shown that the red-green color weakness color image enhancement method based on the visual perception characteristics of abnormal color vision requires designing different degrees of enhanced images according to the severity of color weakness, and simulating different degrees of red weakness and green weakness by shifting the spectral sensitivity curve of normal L or M cones to different degrees, so as to enhance the image. For example, the existing method is based on the principle that the FM100 test is proportional to the severity, generates enhanced images according to the observer's FM100 test score, divides the degree of color weakness into 10 levels, and shifts the cone spectral sensitivity curve at intervals of 2nm to obtain the abnormal cone spectral sensitivity curve.

[0005] The existing methods have the following defects: First, completely different image enhancement methods are used for red-green color blindness and color weakness. It is possible that the enhanced image for red-green color blindness will have a significant change in color, which will be very unpleasant for the red-green color weak. Secondly, it is necessary to obtain images with different enhancement levels according to the severity of color weakness, but in practice it is difficult to accurately obtain the degree of color weakness for each color vision abnormality, which requires the use of relevant professional equipment. In addition, when the accuracy of the severity of color weakness is unknown, the enhancement effect of color images cannot be determined.

[0006] Therefore, there is an urgent need for a color image enhancement method and system for people with red-green color vision abnormalities based on visual perception characteristics to solve the above-mentioned defects in the existing methods. Summary of the invention

[0007] The purpose of the present invention is to provide a color image enhancement method for people with red-green color vision abnormalities based on visual perception characteristics, characterized in that it comprises the following steps:

[0008] Step S1: determining the image enhancement type based on the red-green color vision abnormality type; the red-green color vision abnormality type includes: green weakness, green blindness, severe red weakness, red blindness type and common red weakness; the image enhancement type includes: a first enhancement type, a second enhancement type and a third enhancement type;

[0009] Step S2: generating an abnormal cone spectral sensitivity curve based on the image enhancement type;

[0010] Step S3: measuring the three-primary color spectral radiation distribution of the image display device;

[0011] Step S4: obtaining a conversion matrix from image RGB value to LMS value based on the abnormal cone spectral sensitivity curve and the three-primary color spectral radiation distribution;

[0012] Step S5: Obtain the RGB value of the enhanced image based on the conversion matrix from the image RGB value to the LMS value, thereby achieving color image enhancement.

[0013] The step S1 of determining the image enhancement type based on the red-green color vision abnormality type includes: if the red-green color vision abnormality type is green weakness or green blindness, the image enhancement type is the first enhancement type; if the red-green color vision abnormality type is severe red weakness or red blindness, the image enhancement type is the second enhancement type; if the red-green color vision abnormality type is ordinary red weakness, the image enhancement type is the third enhancement type.

[0014] The specific steps of generating the abnormal cone spectral sensitivity curve in step S2 include:

[0015] Step S21: based on the image enhancement type, obtaining a cone spectral sensitivity curve of a person with normal color vision;

[0016] Step S22: obtaining a scaling function based on the image enhancement type;

[0017] Step S23: Based on the cone spectral sensitivity curve of the normal color vision subject and the proportional function, the abnormal cone spectral sensitivity curve is obtained.

[0018] If the image enhancement type is: the first enhancement type:

[0019] The spectral sensitivity curve of a person with normal color vision is: normal (λ);

[0020] The proportional function is: a first proportional function b(λ);

[0021] The abnormal cone spectral sensitivity curve is: abnormal M cone spectral sensitivity function M d (λ);

[0022] M d (λ)=M normal (λ) / b(λ) (1.1)

[0023] If the image enhancement type is: Second enhancement type:

[0024] The spectral sensitivity curve of a person with normal color vision is: normal (λ);

[0025] The proportional function is: a second proportional function c(λ);

[0026] The abnormal cone spectral sensitivity curve is: Severe abnormal L cone spectral sensitivity function L p (λ);

[0027] L p (λ) = L normal (λ) / c(λ) (1.2)

[0028] If the image enhancement type is: the third enhancement type:

[0029] The spectral sensitivity curve of a person with normal color vision is: normal (λ);

[0030] The proportional function is: a third proportional function d(λ);

[0031] The abnormal cone spectral sensitivity curve is: General abnormal L cone spectral sensitivity function L pa (λ);

[0032] L pa (λ) = L normal(λ) / d(λ)(1.3).

[0033] The conversion matrix Γ from the image RGB value to the LMS value in step S4 is:

[0034]

[0035] The constraints of the transformation matrix Γ are:

[0036]

[0037] Where, L R is the L cone response value corresponding to the red primary color of the image display device, L G is the L cone response value corresponding to the green primary color of the image display device, L B is the L cone response value corresponding to the blue primary color of the image display device, M R is the M cone response value corresponding to the red primary color of the image display device, M G is the M cone response value corresponding to the green primary color of the image display device, M B is the M cone response value corresponding to the blue primary color of the image display device, S R is the S cone response value corresponding to the red primary color of the image display device, S G is the S cone response value corresponding to the green primary color of the image display device, S B is the S cone response value corresponding to the blue primary color of the image display device; ρ L , M and ρ S is the normalization coefficient, which ensures that all different levels of gray for people with normal color vision and people with abnormal color vision, that is, gray in the range of (0, 0, 0) to (1, 1, 1) has exactly the same value in the RGB space and the chromaticity space independent of various devices; L(λ) is the L cone spectral sensitivity function, M(λ) is the M cone spectral sensitivity function, and S(λ) is the S cone spectral sensitivity function.

[0038] The step S5 of obtaining the RGB value of the enhanced image based on the conversion matrix from the image RGB value to the LMS value specifically includes:

[0039] Step S51: input the RGB value of the original image, and perform gamma correction to obtain the corrected RGB value;

[0040] Step S52: Based on the conversion matrix Γ corresponding to the RGB value to the LMS value of the normal color vision person normal , convert the corrected RGB value into LMS value;

[0041] Step S53: Based on the image enhancement type, a color vision abnormality conversion matrix is ​​selected, the LMS value is converted into an enhanced RGB value based on the color vision abnormality conversion matrix, and a gamma operation is performed on the enhanced RGB value to obtain the RGB value of the enhanced image.

[0042] The selecting of the color vision abnormality conversion matrix based on the image enhancement type includes:

[0043] If the image enhancement type is: the first enhancement type:

[0044] The color vision abnormality conversion matrix is: the conversion matrix from RGB values ​​to LMS values ​​corresponding to green blindness or green weakness Г deutan ;

[0045] If the image enhancement type is: Second enhancement type:

[0046] The color vision abnormality conversion matrix is: the conversion matrix from RGB value to LMS value corresponding to severe red deficiency or red blindness Г protan ;

[0047] If the image enhancement type is: the third enhancement type:

[0048] The color vision abnormality conversion matrix is: the conversion matrix Г from RGB value to LMS value corresponding to ordinary red deficiency protanomaly .

[0049] Another object of the present invention is to provide an image enhancement system according to the color image enhancement method for red-green abnormal color vision based on visual perception characteristics of the present invention, characterized in that it comprises:

[0050] Image enhancement type determination module: determines the image enhancement type based on the red-green color vision abnormality type; the red-green color vision abnormality type includes: green weakness, green blindness, severe red weakness, red blindness type and common red weakness; the image enhancement type includes: first enhancement type, second enhancement type and third enhancement type;

[0051] Abnormal cone spectral sensitivity curve generation module: based on the image enhancement type, generates an abnormal cone spectral sensitivity curve;

[0052] Three-primary-color spectral radiation distribution measurement module, measuring the three-primary-color spectral radiation distribution of the image display device;

[0053] Image RGB value to LMS value conversion matrix acquisition module: based on the abnormal cone spectral sensitivity curve and the three primary color spectral radiation distribution, the image RGB value to LMS value conversion matrix is ​​obtained;

[0054] Enhanced image RGB value acquisition module: obtains the RGB value of the enhanced image based on the conversion matrix from image RGB value to LMS value to achieve color image enhancement.

[0055] The image enhancement type determination module: determining the image enhancement type based on the red-green color vision abnormality type includes:

[0056] If the red-green color vision abnormality type is green color weakness or green color blindness, the image enhancement type is the first enhancement type; if the red-green color vision abnormality type is severe red color weakness or red color blindness, the image enhancement type is the second enhancement type; if the red-green color vision abnormality type is ordinary red color weakness, the image enhancement type is the third enhancement type.

[0057] The beneficial effects of the present invention are:

[0058] The invention discloses a color image enhancement method for red-green color abnormal color vision persons based on visual perception characteristics, wherein the image enhancement type is determined based on the red-green color abnormality type; based on the image enhancement type, an abnormal cone spectral sensitivity curve is generated; the three-primary color spectral radiation distribution of an image display device is measured; based on the abnormal cone spectral sensitivity curve and the three-primary color spectral radiation distribution, a conversion matrix from an image RGB value to an LMS value is obtained; based on the conversion matrix from an image RGB value to an LMS value, an RGB value of an enhanced image is obtained, thereby realizing color image enhancement.

[0059] The problem that the prior art uses a specific image enhancement algorithm for red-green color blindness and color weakness, which results in a significant change in color of the enhanced image for red-green color blindness and color weakness, is overcome. The enhancement method disclosed in the present invention does not require the design of corresponding image enhancement methods for green color blindness and color weakness, respectively. For the enhanced image, green color blindness and color weakness both believe that the enhancement effect is good.

[0060] The problem in the prior art that it is necessary to obtain images with different enhancement levels according to the severity of color weakness, which requires the use of relevant professional equipment, and the enhancement effect of color images cannot be determined when the accuracy of the severity of color weakness is unknown, is overcome. The enhanced image obtained by the enhancement method disclosed in the present invention has a good enhancement effect on different degrees of color weakness, does not require the front end to measure the severity of color weakness, does not require different degrees of enhancement according to the severity of color weakness, can share one enhanced image for people with different degrees of color weakness, and the enhancement effect can also be recognized by them. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a color image enhancement method for people with red-green abnormal color vision based on visual perception characteristics of the present invention;

[0062] Figure 2 A schematic diagram of the working process of an image enhancement system according to an embodiment of the present invention;

[0063] Figure 3is a graphical diagram of a first proportional function b(λ) according to an embodiment of the present invention;

[0064] Figure 4 is a graphical diagram of a second proportional function c(λ) according to an embodiment of the present invention;

[0065] Figure 5 is a graphical diagram of a third proportional function d(λ) according to an embodiment of the present invention;

[0066] Figure 6 This is a schematic diagram of an enhanced image generated by a color image enhancement method for people with red-green color vision abnormalities based on visual perception characteristics according to an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The present invention provides a color image enhancement method and system for red-green color vision abnormalities based on visual perception characteristics. The present invention is further described in detail below in conjunction with the accompanying drawings.

[0068] like Figure 1 The embodiment of the present invention shown discloses a color image enhancement method for red-green abnormal color vision based on visual perception characteristics, comprising:

[0069] Step S1: determining the image enhancement type based on the red-green color vision abnormality type; the red-green color vision abnormality type includes: green weakness, green blindness, severe red weakness, red blindness type and common red weakness; the image enhancement type includes: a first enhancement type, a second enhancement type and a third enhancement type;

[0070] Step S2: generating an abnormal cone spectral sensitivity curve based on the image enhancement type;

[0071] Step S3: measuring the three-primary color spectral radiation distribution of the image display device;

[0072] Step S4: obtaining a conversion matrix from image RGB value to LMS value based on the abnormal cone spectral sensitivity curve and the three-primary color spectral radiation distribution;

[0073] Step S5: Obtain the RGB value of the enhanced image based on the conversion matrix from the image RGB value to the LMS value, thereby achieving color image enhancement.

[0074] In this embodiment, the group of people with abnormal red and green color vision is divided into three types, the first type is green color blindness and green weakness, the second type is ordinary red weakness, and the third type is severe red weakness and red blindness. According to the characteristics of the three types of abnormal color vision, an image enhancement method is designed to enable all individuals in each type to obtain a relatively good enhancement effect as much as possible. Different from the prior art that requires the use of professional measurement equipment and measurement methods, such as FM100 test, and generates enhanced images based on the FM100 test scores of the observer, the color image enhancement method for people with abnormal red and green color vision based on visual perception characteristics disclosed in the present invention obtains the spectral sensitivity function of abnormal L or M cones by dividing the spectral sensitivity function of normal L or M cones by a coefficient function, and then calculates the conversion matrix from RGB value to LMS value for abnormal color vision according to the product of the spectral sensitivity function and the three primary color radiation spectrum of the terminal display device, thereby achieving color image enhancement. The present invention does not require high accuracy of the front-end red-green color vision detection system, that is, it only needs to divide the red-green color vision users into three types, and does not require the degree of color weakness to obtain an image that the corresponding individuals think has a good enhancement effect.

[0075] The specific implementation process of each step is introduced below.

[0076] Step S1: determining the image enhancement type based on the red-green color vision abnormality type; the red-green color vision abnormality type includes: green weakness, green blindness, severe red weakness, red blindness type and common red weakness; the image enhancement type includes: a first enhancement type, a second enhancement type and a third enhancement type;

[0077] Determining the image enhancement type based on the red-green color vision abnormality type in step S1 includes:

[0078] If the red-green color vision abnormality type is green weakness or green blindness, the image enhancement type is the first enhancement type;

[0079] If the red-green color vision abnormality type is severe red-green deficiency or red-green blindness, the image enhancement type is the second enhancement type;

[0080] If the red-green color vision abnormality type is common red-green deficiency, the image enhancement type is the third enhancement type;

[0081] In this embodiment, the accuracy requirement of the front-end red-green abnormal color vision detection system is not high, and there is no need to obtain a specific degree of color weakness. It is only necessary to divide the red-green abnormal color vision group into the first enhancement type, the second enhancement type, and the third enhancement type. Those skilled in the art should know the use of relevant detection methods in the prior art, such as Yu Ziping's color blindness test chart, Farnsworth D-15, Farnsworth-Munsell100-Hue, and Neitz color blindness test mirror (OT-Ⅱ) test, etc. In this embodiment, the specific detection method is not specifically limited. It avoids the problem that it is difficult to accurately obtain the color weakness degree of color vision abnormalities in actual application.

[0082] Step S2: generating an abnormal cone spectral sensitivity curve based on the image enhancement type;

[0083] The specific steps of generating the abnormal cone spectral sensitivity curve in step S2 include:

[0084] Step S21: based on the image enhancement type, obtaining a cone spectral sensitivity curve of a person with normal color vision;

[0085] Step S22: obtaining a scaling function based on the image enhancement type;

[0086] Step S23: Based on the cone spectral sensitivity curve of the normal color vision subject and the proportional function, the abnormal cone spectral sensitivity curve is obtained.

[0087] In this embodiment, the preset L, M, and S cone spectral sensitivity functions of normal color vision are L normal (λ), M normal (λ), S normal (λ) indicates that those skilled in the art should know that the preset L, M, and S cone spectral sensitivity functions of normal color vision subjects are: the values ​​defined by Stockman and Sharpe (2000) or the values ​​defined by Smith and Pokorny (1975). In this embodiment, the spectral sensitivity curve of normal color vision subjects is L normal (λ), M normal Choose one of (λ).

[0088] If the image enhancement type is: the first enhancement type:

[0089] The spectral sensitivity curve of a person with normal color vision is: normal (λ);

[0090] The proportional function is: a first proportional function b(λ);

[0091] The abnormal cone spectral sensitivity curve is: abnormal M cone spectral sensitivity function M d (λ);

[0092] M d (λ)=M normal (λ) / b(λ) (1.1)

[0093] In this embodiment, the preset M cone spectral sensitivity function M of a person with normal color vision is normal (λ) is: the value defined by Stockman and Sharpe (2000) or the value defined by Smith and Pokorny (1975);

[0094] In this embodiment, the graph of the first proportional function b(λ) is as follows: Figure 3 As shown in FIG. 1 , this value is obtained by analyzing the experimental data obtained after subjective evaluation experiments on color images with different enhancement degrees by people with red-green abnormal color vision. The data of the first proportional function b(λ) are shown in Table 1. The enhanced color image derived from the abnormal M-cone spectral sensitivity function corresponding to this value has a good enhancement effect on both green-blindness and green-weakness.

[0095]

[0096] If the image enhancement type is: Second enhancement type:

[0097] The spectral sensitivity curve of a person with normal color vision is: normal (λ);

[0098] The proportional function is: a second proportional function c(λ);

[0099] The abnormal cone spectral sensitivity curve is: Severe abnormal L cone spectral sensitivity function L p (λ);

[0100] L p (λ)=L normal (λ) / c(λ) (1.2)

[0101] In this embodiment, the preset L cone spectral sensitivity function L of a person with normal color vision is normal (λ) is: the value defined by Stockman and Sharpe (2000) or the value defined by Smith and Pokorny (1975);

[0102] In this embodiment, the graph of the second proportional function c(λ) is as follows: Figure 4As shown in FIG. 2 , this value is obtained by analyzing the experimental data obtained after subjective evaluation experiments on color images with different enhancement degrees by red-green abnormal color vision subjects. The data of the second proportional function c(λ) are shown in Table 2. The enhanced color image derived from the abnormal L-cone spectral sensitivity function corresponding to this value has a good enhancement effect on severe red-green deficiency and red-green blindness.

[0103]

[0104] If the image enhancement type is: the third enhancement type:

[0105] The spectral sensitivity curve of a person with normal color vision is: normal (λ);

[0106] The proportional function is: a third proportional function d(λ);

[0107] The abnormal cone spectral sensitivity curve is: General abnormal L cone spectral sensitivity function L pa (λ);

[0108] L pa (λ) = L normal (λ) / d(λ) (1.3)

[0109] In this embodiment, the graph of the third proportional function d(λ) is as follows: Figure 5 As shown in FIG. 3 , this value is obtained by analyzing the experimental data obtained after subjective evaluation experiments on color images with different enhancement degrees by red-green abnormal color vision subjects. The data of the third proportional function d(λ) are shown in Table 3. The enhanced color image derived from the abnormal L-cone spectral sensitivity function corresponding to this value has a good enhancement effect for ordinary red-weak people.

[0110]

[0111] Step S3: measuring the three-primary color spectrum radiance curve of the image display device;

[0112] In this embodiment, the spectral radiance curves of the three primary colors of red, green and blue of the image display device are measured by professional optical equipment. The spectral radiance curves of the three primary colors include: the spectral radiance curve of the red primary color Spectral radiance curve of green primary color And the spectral radiance curve of the blue primary color

[0113] Step S4: obtaining a conversion matrix from image RGB value to LMS value based on the abnormal cone spectral sensitivity curve and the three-primary color spectral radiation distribution;

[0114] The conversion matrix Γ from the image RGB value to the LMS value in step S4 is:

[0115]

[0116] The constraints of the transformation matrix Γ are:

[0117]

[0118] Where, L R is the L cone response value corresponding to the red primary color of the image display device, L G is the L cone response value corresponding to the green primary color of the image display device, L B is the L cone response value corresponding to the blue primary color of the image display device, M R is the M cone response value corresponding to the red primary color of the image display device, M G is the M cone response value corresponding to the green primary color of the image display device, M B is the M cone response value corresponding to the blue primary color of the image display device, S R is the S cone response value corresponding to the red primary color of the image display device, S G is the S cone response value corresponding to the green primary color of the image display device, S B is the S cone response value corresponding to the blue primary color of the image display device; ρ L , M and ρ S is the normalization coefficient, which ensures that all different levels of gray for people with normal color vision and people with abnormal color vision, that is, gray in the range of (0, 0, 0) to (1, 1, 1) has exactly the same value in the RGB space and the chromaticity space independent of various devices; L(λ) is the L cone spectral sensitivity function, M(λ) is the M cone spectral sensitivity function, and S(λ) is the S cone spectral sensitivity function;

[0119] ρ L , M and ρ S is the normalization coefficient, which is used to satisfy the constraints of the transformation matrix Γ defined in formula (3).

[0120] When the L cone spectral sensitivity function L(λ) is the L cone spectral sensitivity function L of a person with normal color vision normal (λ); and the M cone spectral sensitivity function M(λ) is the M cone spectral sensitivity function M of a person with normal color vision normal (λ); and the S cone spectral sensitivity function S(λ) is the S cone spectral sensitivity function S of a person with normal color vision normal (λ); through equations (2), (3) and (4), we can obtain the conversion matrix Г from RGB value to LMS value corresponding to normal color vision normal ;

[0121] When the L cone spectral sensitivity function L(λ) is the L cone spectral sensitivity function L of a person with normal color vision normal (λ); and the M-cone spectral sensitivity function M(λ) is the abnormal M-cone spectral sensitivity function M d (λ); and the S cone spectral sensitivity function S(λ) is the S cone spectral sensitivity function S of a person with normal color vision normal (λ); through equations (2), (3) and (4), the conversion matrix Г from RGB value to LMS value corresponding to green blindness or green weakness is obtained deutan ;

[0122] When the L cone spectral sensitivity function L(λ) is a severely abnormal L cone spectral sensitivity function L p (λ); and the M cone spectral sensitivity function M(λ) is the M cone spectral sensitivity function M of a person with normal color vision normal (λ); and the S cone spectral sensitivity function S(λ) is the S cone spectral sensitivity function S of a person with normal color vision normal (λ); Through equations (2), (3) and (4), the conversion matrix Г from RGB value to LMS value corresponding to severe red-light deficiency or red-light blindness is obtained protan ;

[0123] When the L cone spectral sensitivity function L(λ) is the general abnormal L cone spectral sensitivity function L pa (λ); and the M cone spectral sensitivity function M(λ) is the M cone spectral sensitivity function M of a person with normal color vision normal (λ); and the S cone spectral sensitivity function S(λ) is the S cone spectral sensitivity function S of a person with normal color vision normal (λ); through equations (2), (3) and (4), the conversion matrix Г corresponding to the RGB value to the LMS value of the common red weak protanomaly ;

[0124] Step S5: Obtain the RGB value of the enhanced image based on the conversion matrix from the image RGB value to the LMS value, thereby achieving color image enhancement.

[0125] The step S5 of obtaining the RGB value of the enhanced image based on the conversion matrix from the image RGB value to the LMS value specifically includes:

[0126] Step S51: input the RGB value of the original image, and perform gamma correction to obtain the corrected RGB value;

[0127] Step S52: Based on the conversion matrix Γ corresponding to the RGB value to the LMS value of the normal color vision person normal , convert the corrected RGB value into LMS value;

[0128] Step S53: Based on the image enhancement type, a color vision abnormality conversion matrix is ​​selected, the LMS value is converted into an enhanced RGB value based on the color vision abnormality conversion matrix, and a gamma operation is performed on the enhanced RGB value to obtain the RGB value of the enhanced image.

[0129] Based on the image enhancement type, selecting a color vision abnormality conversion matrix includes:

[0130] If the image enhancement type is: the first enhancement type:

[0131] The color vision abnormality conversion matrix is: the conversion matrix from RGB values ​​to LMS values ​​corresponding to green blindness or green weakness Г deutan ;

[0132] If the image enhancement type is: Second enhancement type:

[0133] The color vision abnormality conversion matrix is: the conversion matrix from RGB value to LMS value corresponding to severe red deficiency or red blindness Г protan ;

[0134] If the image enhancement type is: the third enhancement type:

[0135] The color vision abnormality conversion matrix is: the conversion matrix Г from RGB value to LMS value corresponding to ordinary red deficiency protanomaly .

[0136] Another embodiment of the present invention discloses an image enhancement system according to the color image enhancement method for red-green color vision abnormalities based on visual perception characteristics of the present invention, comprising:

[0137] Image enhancement type determination module: determines the image enhancement type based on the red-green color vision abnormality type; the red-green color vision abnormality type includes: green weakness, green blindness, severe red weakness, red blindness type and common red weakness; the image enhancement type includes: first enhancement type, second enhancement type and third enhancement type;

[0138] Abnormal cone spectral sensitivity curve generation module: based on the image enhancement type, generates an abnormal cone spectral sensitivity curve;

[0139] Three-primary-color spectral radiation distribution measurement module, measuring the three-primary-color spectral radiation distribution of the image display device;

[0140] Image RGB value to LMS value conversion matrix acquisition module: based on the abnormal cone spectral sensitivity curve and the three primary color spectral radiation distribution, the image RGB value to LMS value conversion matrix is ​​obtained;

[0141] Enhanced image RGB value acquisition module: obtains the RGB value of the enhanced image based on the conversion matrix from image RGB value to LMS value to achieve color image enhancement.

[0142] The image enhancement type determination module: determining the image enhancement type based on the red-green color vision abnormality type includes:

[0143] If the red-green color vision abnormality type is green color weakness or green color blindness, the image enhancement type is the first enhancement type; if the red-green color vision abnormality type is severe red color weakness or red color blindness, the image enhancement type is the second enhancement type; if the red-green color vision abnormality type is ordinary red color weakness, the image enhancement type is the third enhancement type.

[0144] In this embodiment, the workflow of the image enhancement system according to the color image enhancement method for red-green abnormal color vision based on visual perception characteristics of the present invention is as follows: Figure 2 As shown, the color blindness and color weakness detection system at the front end only needs to determine the red-green color vision abnormality type of the color blindness and color weakness person, and the red-green color vision abnormality type includes: green weakness, green blindness, severe red weakness, red blindness type and ordinary red weakness; there is no need to identify the severity of the specific color blindness and color blindness. Image enhancement type determination module: determines the image enhancement type based on the red-green color vision abnormality type; the image enhancement type includes: the first enhancement type, the second enhancement type and the third enhancement type;

[0145] Under the cooperation of the image enhancement type determination module, the abnormal cone spectral sensitivity curve generation module, the three-primary color spectral radiation distribution measurement module, the image RGB value to LMS value conversion matrix acquisition module and the enhanced image RGB value acquisition module, three processing links are formed corresponding to the three image enhancement types. Figure 2 Image enhancement module 1, image enhancement module 2 and image enhancement module 3.

[0146] If the red-green color vision abnormality type is green weakness or green blindness, the image enhancement type is the first enhancement type, and the image enhancement module 1 link is executed;

[0147] If the red-green color vision abnormality type is severe red weakness or red blindness, the image enhancement type is the second enhancement type, and the image enhancement module 2 link is executed;

[0148] If the red-green color vision abnormality type is ordinary red weakness, the image enhancement type is the third enhancement type, and the image enhancement module 3 link is executed.

[0149] In order to verify the effectiveness of the color image enhancement method and system for red-green abnormal color vision persons based on visual perception characteristics disclosed in the present invention, the following verification experiment was conducted.

[0150] A total of 15 people with red-green color vision abnormalities and 4 people with normal color vision participated in the experiment, aged between 20 and 25. The 15 people with red-green color vision abnormalities were classified into 5 green-green weak, 4 red-green weak, 4 green-green blind and 2 red-green blind after the following four tests: Yu Ziping's color blindness test chart, Farnsworth D-15, Farnsworth-Munsell 100-Hue and Neitz color blindness test mirror (OT-Ⅱ) test.

[0151] The experiment used 23 original images as stimuli, and the scenes in the images included various colors as much as possible, such as Figure 5 These images are from the Kodak test image library, the sRGB color images corresponding to the multispectral images of the Columbia University Color Vision Laboratory, and the sRGB color images corresponding to the multispectral images of the Munsell Color Science Laboratory. In each experiment, the original image and the enhanced image are displayed side by side on a gray background with (R, G, B) = (128, 128, 128) and a brightness value of 16.5 cd / m2.

[0152] The observer first adapts to the dark room for 3 minutes before entering the test. Each experiment includes 23 trials, corresponding to 23 images, and the order of image appearance is random. In each trial, the left (or right) is the original image, and the right (or left) is the enhanced image. The initial value of the enhanced image is the same as the original image without any enhancement. The observer adjusts the enhancement degree of the enhanced image through the button. For each image, the enhanced image is an image with different enhancement degrees generated by Yang's algorithm for red weakness or green weakness and an image generated by the algorithm of this patent. The observer's task is to "adjust the enhancement degree of the enhanced image so that the enhanced image has the best enhancement effect compared with the original image on the left, that is, the image contrast is the strongest and the image is very natural." Contrast and naturalness are the two most important factors for measuring the effect of enhanced images. Contrast can be understood as the difference within a local area or the difference between farther areas in the image, such as the color difference between objects. When the contrast of an image is enhanced, it is easy to make the color of the image look unnatural in the eyes of red and green weak eyes. Therefore, the preservation of naturalness is also an important factor in evaluating the effectiveness of an algorithm. Here, the observer's task is to select an enhanced image that takes into account both contrast and naturalness.

[0153] Each observer repeated the experiment 6 times. For each observer, the best image enhancement effect for each image was the average of the 6 experiments, and the best image enhancement degree was the average of the 23 images.

[0154] The experimental results show that the best enhanced images obtained by each group of observers in the experiment are the enhanced images generated by the method in this patent. The best enhanced images corresponding to deuteranopia and deuteranopia are basically the same, that is, the degree of enhancement is basically the same, which is the image generated by the algorithm of module 1 in this patent. The best enhanced images corresponding to protanopia and protanopia are different. The best enhanced images obtained by three ordinary protanopia patients are the images generated by module 3 of this patent, and the best enhanced images corresponding to one severe protanopia patient and protanopia patient are the images generated by module 2 of this patent. The best enhanced images selected by normal color vision subjects in the experiment are very close to deuteranopia and deuteranopia, indicating that when image enhancement is performed for color vision abnormalities such as deuteranopia and deuteranopia, it will not hinder the perception of the image by normal color vision subjects. The experiment shows that there is no correlation between the total error score of the FM 100-Hue test of color vision abnormalities and the image with the best enhancement effect they selected, and the degree of image enhancement cannot be directly predicted by the total error score of the FM 100-Hue test.

[0155] In summary, in order to overcome the following technical problems existing in the prior art when enhancing color images for people with red-green color vision abnormalities:

[0156] (1) The existing technology needs to design corresponding enhancement algorithms for red-green color blindness, green-green color blindness, red-green weakness, and green-green weakness respectively; as a result, the enhanced image for red-green color blindness has a large change in color, which makes the subjective experience of red-green color weak people very bad. Relevant data show that among color-blind people, the number of red-green color weak people accounts for about 75% of the total number of color-blind people, further highlighting the impact of this problem on most color-blind people.

[0157] (2) The existing technology requires different degrees of color image enhancement according to the severity of color deficiency in order to achieve a good enhancement effect for red and green vision deficiency. This results in the need to use relevant professional equipment to accurately obtain the color deficiency degree of each color vision deficiency patient, so that the color deficiency patients with corresponding severity can obtain a better color image enhancement effect. Considering the poor portability of relevant professional equipment, it needs to be set up in a professional laboratory, which greatly increases the difficulty for ordinary color vision deficiency patients to accurately obtain the color deficiency severity, further highlighting the problem that the enhancement effect of color images cannot be determined when the accuracy of the color deficiency severity is unknown.

[0158] The color image enhancement method for people with red-green color abnormality based on visual perception characteristics disclosed in the present invention has the following advantages:

[0159] (1) Although different enhancement algorithms need to be designed for red-green deficiency and red-green blindness, a single enhancement algorithm can be used for green-green blindness and green-green deficiency.

[0160] In this embodiment, for red vision abnormality, there are still two types of image enhancement methods, one for common red vision deficiency and the other for severe red vision deficiency and red vision blindness. The difference between the two is the degree of enhancement. For green vision abnormality, that is, green blindness and green vision deficiency, only one type of image enhancement method is used. The advantage of the present invention is that there is no need to design corresponding image enhancement methods for green vision deficiency and color deficiency respectively. For the enhanced image, green vision deficiency and color deficiency both believe that the enhancement effect is good.

[0161] (2) The enhanced image obtained by the algorithm has a good enhancement effect on different degrees of color weakness. There is no need for the front end to measure the severity of color weakness, and there is no need to perform different degrees of enhancement based on the severity of color weakness. Instead, all color weak people share the same enhanced image, but the enhancement effect can still be recognized.

[0162] The enhanced image generated by the color image enhancement method for red-green abnormal color vision based on visual perception characteristics disclosed in the present invention is as follows: Figure 6 As shown. In this embodiment, the accuracy requirement for the front-end red-green abnormal color vision detection system is not high. It is only necessary to distinguish red-green blindness, severe red-green weakness, ordinary red-green weakness, green-green weakness and green-green blindness. It is not required to obtain the degree of color weakness, and an image that the corresponding individuals think has a good enhancement effect can be obtained. A default value can be provided for the color-weak color image enhancement system in the terminal display device. The enhanced image generated based on the default value is considered by many abnormal color vision individuals to have the best enhancement effect. There is no need to generate an enhanced image based on the accurate severity of the user's personal color weakness, and an enhanced image that is recognized by all individuals of the abnormal color vision type and has a good enhancement effect can be directly obtained.

Claims

1. A color image enhancement method for people with red-green color vision abnormalities based on visual perception characteristics, characterized in that: The steps include: Step S1: determining the image enhancement type based on the red-green color vision abnormality type; The red-green color vision abnormality types include: green weakness, green color blindness, severe red weakness, red color blindness and common red weakness; the image enhancement types include: first enhancement type, second enhancement type and third enhancement type; Step S2: generating an abnormal cone spectral sensitivity curve based on the image enhancement type; Step S3: measuring the three-primary color spectral radiation distribution of the image display device; Step S4: obtaining a conversion matrix from image RGB value to LMS value based on the abnormal cone spectral sensitivity curve and the three-primary color spectral radiation distribution; Step S5: Obtain the RGB value of the enhanced image based on the conversion matrix from the image RGB value to the LMS value, thereby achieving color image enhancement.

2. The color image enhancement method for red-green color vision abnormalities based on visual perception characteristics according to claim 1, characterized in that: The step S1 of determining the image enhancement type based on the red-green color vision abnormality type includes: if the red-green color vision abnormality type is green weakness or green blindness, the image enhancement type is the first enhancement type; if the red-green color vision abnormality type is severe red weakness or red blindness, the image enhancement type is the second enhancement type; if the red-green color vision abnormality type is ordinary red weakness, the image enhancement type is the third enhancement type.

3. The color image enhancement method for people with red-green color vision abnormality based on visual perception characteristics according to claim 1, characterized in that: The specific steps of generating the abnormal cone spectral sensitivity curve in step S2 include: Step S21: based on the image enhancement type, obtaining a cone spectral sensitivity curve of a person with normal color vision; Step S22: obtaining a scaling function based on the image enhancement type; Step S23: Based on the cone spectral sensitivity curve of the normal color vision subject and the proportional function, the abnormal cone spectral sensitivity curve is obtained.

4. The color image enhancement method for people with red-green color vision abnormalities based on visual perception characteristics according to claim 3, characterized in that: If the image enhancement type is: the first enhancement type: The spectral sensitivity curve of a person with normal color vision is: normal (λ); The proportional function is: a first proportional function b(λ); The abnormal cone spectral sensitivity curve is: abnormal M cone spectral sensitivity function M d (λ); M d (λ)=M normal (λ) / b(λ) (1.1) If the image enhancement type is: Second enhancement type: The spectral sensitivity curve of a person with normal color vision is: normal (λ); The proportional function is: a second proportional function c(λ); The abnormal cone spectral sensitivity curve is: Severe abnormal L cone spectral sensitivity function L p (λ); L p (λ)=L normal (λ) / c(λ) (1.2) If the image enhancement type is: the third enhancement type: The spectral sensitivity curve of a person with normal color vision is: normal (λ); The proportional function is: a third proportional function d(λ); The abnormal cone spectral sensitivity curve is: General abnormal L cone spectral sensitivity function L pa (λ); L pa (λ)=L normal (λ) / d(λ) (1.3).

5. The color image enhancement method for red-green color vision abnormalities based on visual perception characteristics according to claim 1, characterized in that: The conversion matrix Γ from the image RGB value to the LMS value in step S4 is: The constraints of the transformation matrix Γ are: Where, L R is the L cone response value corresponding to the red primary color of the image display device, L G is the L cone response value corresponding to the green primary color of the image display device, L B is the L cone response value corresponding to the blue primary color of the image display device, M R is the M cone response value corresponding to the red primary color of the image display device, M G is the M cone response value corresponding to the green primary color of the image display device, M B is the M cone response value corresponding to the blue primary color of the image display device, S R is the S cone response value corresponding to the red primary color of the image display device, S G is the S cone response value corresponding to the green primary color of the image display device, S B is the S cone response value corresponding to the blue primary color of the image display device; ρ L , M and ρ S is the normalization coefficient, which ensures that all different levels of gray for people with normal color vision and people with abnormal color vision, that is, gray in the range of (0, 0, 0) to (1, 1, 1) has exactly the same value in the RGB space and the chromaticity space independent of various devices; L(λ) is the L cone spectral sensitivity function, M(λ) is the M cone spectral sensitivity function, and S(λ) is the S cone spectral sensitivity function.

6. The color image enhancement method for red-green color vision abnormalities based on visual perception characteristics according to claim 1, characterized in that: The step S5 of obtaining the RGB value of the enhanced image based on the conversion matrix from the image RGB value to the LMS value specifically includes: Step S51: input the RGB value of the original image, and perform gamma correction to obtain the corrected RGB value; Step S52: Based on the conversion matrix Γ corresponding to the RGB value to the LMS value of the normal color vision person normal , convert the corrected RGB value into LMS value; Step S53: Based on the image enhancement type, a color vision abnormality conversion matrix is ​​selected, the LMS value is converted into an enhanced RGB value based on the color vision abnormality conversion matrix, and a gamma operation is performed on the enhanced RGB value to obtain the RGB value of the enhanced image.

7. The color image enhancement method for red-green color vision abnormalities based on visual perception characteristics according to claim 6, characterized in that: The selecting of the color vision abnormality conversion matrix based on the image enhancement type includes: If the image enhancement type is: the first enhancement type: The color vision abnormality conversion matrix is: the conversion matrix from RGB values ​​to LMS values ​​corresponding to green blindness or green weakness Г deutan ; If the image enhancement type is: Second enhancement type: The color vision abnormality conversion matrix is: the conversion matrix from RGB value to LMS value corresponding to severe red deficiency or red blindness Г protan ; If the image enhancement type is: the third enhancement type: The color vision abnormality conversion matrix is: the conversion matrix Г from RGB value to LMS value corresponding to ordinary red deficiency protanomaly .

8. An image enhancement system for the color image enhancement method for red-green color vision abnormalities based on visual perception characteristics according to claim 1, characterized in that: include: Image enhancement type determination module: determines the image enhancement type based on the red and green color vision abnormality type; The red-green color vision abnormality types include: green weakness, green blindness, severe red weakness, red blindness type and common red weakness; the image enhancement types include: first enhancement type, second enhancement type and third enhancement type; Abnormal cone spectral sensitivity curve generation module: based on the image enhancement type, generates an abnormal cone spectral sensitivity curve; Three-primary-color spectral radiation distribution measurement module, measuring the three-primary-color spectral radiation distribution of the image display device; Image RGB value to LMS value conversion matrix acquisition module: based on the abnormal cone spectral sensitivity curve and the three primary color spectral radiation distribution, the image RGB value to LMS value conversion matrix is ​​obtained; Enhanced image RGB value acquisition module: obtains the RGB value of the enhanced image based on the conversion matrix from image RGB value to LMS value to achieve color image enhancement.

9. The color image enhancement method for red-green color vision abnormalities based on visual perception characteristics according to claim 1, characterized in that: The image enhancement type determination module: determining the image enhancement type based on the red-green color vision abnormality type includes: If the red-green color vision abnormality type is green color weakness or green color blindness, the image enhancement type is the first enhancement type; if the red-green color vision abnormality type is severe red color weakness or red color blindness, the image enhancement type is the second enhancement type; if the red-green color vision abnormality type is ordinary red color weakness, the image enhancement type is the third enhancement type.

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