Image pseudo-color fusion method and system under dark background based on polarization angle characteristics

Abstract

This invention provides a pseudo-color fusion method and system for images against a dark background based on polarization angle characteristics. The method includes: acquiring the outgoing light intensity of the target scene using a polarization camera, calculating the Stokes parameter image data of the target scene, and parsing the polarization information; mapping the synthesized light intensity I, linear polarization degree P, and polarization angle A to the brightness V, saturation S, and hue H in the HSV model, respectively, to obtain normalized synthesized light intensity, linear polarization degree stretching data, polarization angle preprocessing data, and hue matching parameters; processing the characteristic data of polarization angle A using a preset polarization robustness discrimination logic for the current observation point in the target scene to obtain polarization robustness discrimination parameters; judging the state of polarization noise based on the polarization robustness discrimination parameters and performing pseudo-color model mapping; and converting the pseudo-color HSV image into an RGB color representation image for display. This invention solves the technical problem of unsatisfactory pseudo-color fusion results caused by excessively dark background areas where the target is located.

Description

Technical Field

[0001] This invention relates to the field of target recognition and imaging technology, specifically to a method and system for pseudo-color fusion of images against a dark background based on polarization angle characteristics. Background Technology

[0002] Targets in dark backgrounds exhibit low contrast, hindering detection and recognition. Polarization imaging, however, can simultaneously acquire target intensity and polarization information, offering advantages in low-light conditions. Furthermore, the human eye's ability to recognize color is significantly superior to its ability to recognize grayscale. Therefore, researchers have proposed numerous pseudo-color fusion methods based on polarization information to improve target detection and recognition efficiency.

[0003] The most classic pseudo-color fusion method is Wolff's HSV model-based mapping method. For example, the existing invention patent application document CN111292279A, "A Polarized Image Visualization Method Based on Color Image Fusion," first converts the polarized image into a three-channel image of light intensity, degree of polarization, and polarization angle; secondly, it decomposes the image into a polarized light color image and a natural light grayscale image; thirdly, it performs image fusion on the two decomposed images, specifically by selecting an appropriate edge-preserving fusion algorithm to fuse the light intensity channels of the two images according to the requirements; then, it normalizes the fused light intensity and polarization angle channels; finally, it projects the normalized image onto the HSV color space to obtain the final visualized polarized color image. The aforementioned existing technology maps the polarization angle to hue, the degree of polarization to saturation, and the synthesized light intensity to brightness. However, if the irradiance of the target image is relatively low, i.e., the synthesized light intensity component is low, then the color characteristics of the fusion result are not obvious enough, the target enhancement effect is poor, and it is greatly affected by noise. To enhance the pseudo-color fusion effect, Zhou et al. proposed a pseudo-color fusion method based on nonnegative matrix factorization and HSV space, and Xu et al. proposed a pseudo-color image fusion method based on low-rank sparse decomposition. These methods have a certain effect on suppressing background noise, but they only start from the perspective of information processing and do not solve the problem from the perspective of polarization characteristics.

[0004] In summary, the existing technology has the technical problem that the false color blending result is not ideal due to the dark background area where the target is located. Summary of the Invention

[0005] The technical problem to be solved by the present invention is how to solve the problem in the prior art where the background area where the target is located is too dark, resulting in unsatisfactory false color blending results.

[0006] This invention solves the above-mentioned technical problems by employing the following technical solution: a pseudo-color fusion method for images against a dark background based on polarization angle characteristics, comprising:

[0007] S1. Using a polarization camera, collect the outgoing light intensity I(θ) of the target scene in no less than three directions. Based on this, calculate the Stokes parameter image data of the target scene using preset logic. Based on this, obtain the polarization information, which includes: linear polarization degree P and polarization angle A.

[0008] S2. Map the synthesized light intensity I, linear polarization degree P, and polarization angle A to the brightness V, saturation S, and hue H in the HSV model, respectively, to obtain the pseudo-color model mapping data. The pseudo-color model mapping data includes: normalized synthesized light intensity, linear polarization degree stretching data, polarization angle preprocessing data, and hue matching parameters.

[0009] S3. Process the characteristic data of polarization angle A using the preset polarization robustness discrimination logic to obtain polarization robustness discrimination parameters;

[0010] S4. Determine the state of polarization noise based on the robustness discrimination parameter. When the polarization noise is less than the preset polarization noise threshold, take the maximum value of the normalized composite light intensity and linear polarization degree stretching data to map it to the brightness V; when the polarization noise is greater than or equal to the preset polarization noise threshold, the normalized composite light intensity... Mapping to lightness V, and using that to obtain a pseudo-color HSV image;

[0011] S5. Convert the pseudo-color HSV color representation to RGB color representation to display the pseudo-color image on the computer.

[0012] This invention fully considers the physical meaning of polarization degree and polarization angle during the preprocessing of P and A, preserving as much multidimensional information of polarization parameters as possible to provide good data support for subsequent data processing. This invention targets dark regions in polarized images that tend to have stronger polarization characteristics. Simultaneously, this invention utilizes operations such as normalization of light intensity and stretching of polarization data to make the color features after pseudo-color fusion more obvious than existing technologies. It improves the situation where the pseudo-color fusion result is unsatisfactory due to the dark background area where the target is located. This invention determines whether a certain region is a region with strong polarization characteristics or a region with polarization noise, thereby highlighting the polarization characteristics of the target.

[0013] In a more specific technical solution, in step S1, the emitted light intensity I(θ) of the target scene is acquired using a polarization camera, and the Stokes parameter image data is obtained: the emitted light intensities I(θ1), I(θ2), and I(θ3) in three directions of the target scene are acquired using a polarization camera, and then substituted into the formula... The synthesized light intensity I and the linear polarization parameters Q and U can be obtained; where I is the synthesized light intensity, and Q and U are the linear polarization parameters.

[0014] In a more specific technical solution, in step S1, the linear polarization degree P and polarization angle A are obtained analytically using the following logic:

[0015]

[0016] .

[0017] In a more specific technical solution, in step S2, the emitted light intensity is normalized using the following logic to transform the range of the emitted light intensity to a suitable light intensity range, thereby obtaining the normalized composite light intensity.

[0018]

[0019] In a more specific technical solution, step S2 involves stretching the linear polarization degree P using the following logic to stretch it to the applicable range of linear polarization degree values, thereby obtaining stretched linear polarization degree data.

[0020]

[0021] This invention addresses the characteristic that in practical application scenarios, the linear polarization degree of ground objects is mostly below 10%. It utilizes linear polarization degree stretching logic to stretch the linear polarization degree, thereby improving image contrast.

[0022] In a more specific technical solution, in step S2, the polarization angle A is processed using the following logic to obtain preprocessed polarization angle data:

[0023]

[0024] This invention addresses the issue that the value range of the arctan() function and the polarization angle processing results do not match the physical meaning of the polarization angle (the polarization angle's range is [0,π]). It replaces the arctan() function with the atan2() function, which more comprehensively considers the physical meaning of the polarization angle and improves the consistency between the fusion effect and the actual situation.

[0025] In a more specific technical solution, step S2 includes: multiplying the polarization angle A by a preset coefficient using the following logic to match the value range of the hue H, thereby obtaining the hue matching parameter φ:

[0026]

[0027] In a more specific technical solution, in step S3, the following logic is used to perform polarization robustness discrimination to obtain the polarization robustness discrimination parameter Δ:

[0028]

[0029]

[0030] Δ = close(open(Δ,B),B) (15)

[0031] Where, f1 = sin 2 φ, f2=cos 2 φ and f1(x,y) correspond to a pixel in image f1, and M is the number of pixels in the neighborhood S (including the center point). Here, the neighborhood S is an eight-neighborhood, so M = 9. This indicates that the neighborhood average with a template of 3*3 is performed on f1. g is the polarization robustness characterization parameter of the current point, t is the discrimination threshold, close and open are morphological opening and closing operations, and B is the structuring element.

[0032] This invention utilizes the characteristics of the polarization angle to determine polarization robustness. Specifically, when the measurement robustness of the polarization degree relative to the linear Stokes parameter is poor, the polarization angle statistic approximates a Gaussian distribution; conversely, when the measurement robustness of the polarization degree relative to the linear Stokes parameter is good, the AOP (Average Polarization Angle) approximates a uniform distribution. Therefore, this characteristic of the polarization angle AOP can be used to estimate whether a certain region contains polarization noise. Based on the polarization robustness determination under dark backgrounds using the polarization angle characteristics, the pseudo-color fusion effect of images is further optimized.

[0033] In a more specific technical solution, step S4 utilizes the following logic for pseudo-color model mapping:

[0034] H=φ (10)

[0035]

[0036] .

[0037] This invention uses polarization robustness discrimination parameters to determine the polarization robustness and polarization noise of a specific region, which serves as a criterion for pseudo-color model mapping operations. This approach highlights regions with low reflectivity and high polarization characteristics while avoiding widespread pseudo-polarization phenomena.

[0038] In more specific technical solutions, image pseudo-color fusion systems based on polarization angle characteristics against dark backgrounds include:

[0039] The polarization information analysis module is used to acquire the outgoing light intensity I(θ) of the target scene in at least three directions using a polarization camera, calculate the Stokes parameter image data of the target scene using preset logic, and analyze the polarization information, which includes: linear polarization degree P and polarization angle A.

[0040] The data preprocessing module is used to map the synthesized light intensity I, linear polarization degree P, and polarization angle A to the brightness V, saturation S, and hue H in the HSV model, respectively, to obtain the pseudo-color model mapping applicable data. The pseudo-color model mapping applicable data includes: normalized synthesized light intensity, linear polarization degree stretching data, polarization angle preprocessing data, and hue matching parameters. The data preprocessing module is connected to the polarization information parsing module.

[0041] The polarization robustness discrimination module processes the characteristic data of polarization angle A using preset polarization robustness discrimination logic to obtain polarization robustness discrimination parameters. The polarization robustness discrimination module is connected to the data preprocessing module.

[0042] The pseudo-color model mapping module is used to determine the state of polarization noise based on the vibration robustness discrimination parameter. When the polarization noise is less than a preset polarization noise threshold, the maximum value of the normalized composite light intensity and linear polarization degree stretching data is taken and mapped to the brightness V. When the polarization noise is greater than or equal to the preset polarization noise threshold, the normalized composite light intensity is... Mapped to lightness V, a pseudo-color HSV image is obtained and connected to the data preprocessing module and the polarization robustness discrimination module;

[0043] The pseudo-color display module is used to convert the pseudo-color HSV color representation to RGB color representation in order to display pseudo-color images on a computer. The pseudo-color display module is connected to the pseudo-color model mapping module.

[0044] Compared with existing technologies, this invention has the following advantages: In the preprocessing of P and A, this invention fully considers the physical meaning of polarization degree and polarization angle, preserving as much multidimensional information of polarization parameters as possible, providing good data support for subsequent data processing. This invention targets dark regions in polarized images that tend to have stronger polarization characteristics. Simultaneously, this invention utilizes operations such as normalization of light intensity and stretching of polarization data to make the color features after pseudo-color fusion more obvious than existing technologies. It improves the situation where the pseudo-color fusion result is unsatisfactory due to the background region being too dark. This invention determines whether a region is a region with strong polarization characteristics or a region with polarization noise, thereby highlighting the polarization characteristics of the target.

[0045] This invention addresses the characteristic that in practical application scenarios, the linear polarization degree of ground objects is mostly below 10%. It utilizes linear polarization degree stretching logic to stretch the linear polarization degree, thereby improving image contrast.

[0046] This invention addresses the issue that the value range of the arctan() function and the polarization angle processing results do not match the physical meaning of the polarization angle (the polarization angle's range is [0,π]). It replaces the arctan() function with the atan2() function, which more comprehensively considers the physical meaning of the polarization angle and improves the consistency between the fusion effect and the actual situation.

[0047] This invention utilizes the characteristics of the polarization angle to determine polarization robustness. Specifically, when the measurement robustness of the degree of polarization relative to the linear Stokes parameter is poor, the polarization angle statistic approximates a Gaussian distribution; conversely, when the measurement robustness of the degree of polarization relative to the linear Stokes parameter is good, the AOP (polarization angle statistic) approximates a uniform distribution. Therefore, this characteristic of the polarization angle can be used to estimate whether a certain region contains polarization noise. Based on the polarization angle characteristic, polarization robustness determination under dark backgrounds further optimizes the pseudo-color fusion effect of images.

[0048] This invention uses polarization robustness discrimination parameters to determine the polarization robustness and polarization noise of a specific region, which serves as a criterion for pseudo-color model mapping operations. This approach highlights regions with low reflectivity and high polarization characteristics while avoiding widespread pseudo-polarization phenomena.

[0049] This invention solves the technical problem in the prior art where the false color blending result is unsatisfactory due to the dark background area where the target is located. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the data processing model for the pseudo-color fusion method for images against a dark background based on polarization angle characteristics, as described in Embodiment 1 of the present invention.

[0051] Figure 2 This is a schematic diagram of the basic steps of the pseudo-color fusion method for images against a dark background based on polarization angle characteristics in Embodiment 1 of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example 1

[0054] like Figure 1 and Figure 2 As shown, the pseudo-color fusion method for images against a dark background based on polarization angle characteristics provided by this invention includes the following basic steps:

[0055] S1, Polarization information analysis;

[0056] In this embodiment, a polarization camera is used to collect the emitted light intensity I(θ) of the target scene in four different directions (0°, 45°, 90°, 135°). In this embodiment, the emitted light intensity I(θ) is calculated using the following logic:

[0057]

[0058] The Stokes parameter image data of the target scene is calculated using the following logic:

[0059]

[0060] Q = I(0°) - I(90°) (6)

[0061] U = I(45°) - I(135°) (7)

[0062] Where I is the synthesized light intensity, Q and U are linear polarization parameters, and the degree of linear polarization P and polarization angle A are then analyzed:

[0063]

[0064]

[0065] S2, Data Preprocessing;

[0066] In this embodiment, the preprocessing of I, P, and A involves converting the physical values ​​into data that can be used for pseudo-color model mapping. I, P, and A are mapped to V, S, and H in the HSV model, respectively, with their value ranges being [0,1], [0,1], and [0,2π]. For I, normalization is performed using formula (6) to transform its value range to [0,1].

[0067]

[0068] For P, since the linear polarization degree of ground objects is generally below 10%, in order to improve the image contrast, we use formula (7) to stretch it, and the range of the stretched value is [0,1].

[0069]

[0070] For A, since the range of the arctan() function is [-π / 2, π / 2], using formula (5) yields A∈[-π / 4, π / 4], which does not conform to the physical meaning of the polarization angle (the range of the polarization angle is [0, π]). Therefore, we use the atan2() function instead of the arctan() function, as shown in formula (8). The range of values ​​for atan2(y,x) is [-π,π]. The angle obtained depends not only on the tangent value y / x, but also on the quadrant in which the point (y,x) is located. Specifically: when the point (x,y) is in the first quadrant, the range of atan2(y,x) is [0,π / 2]; when the point (x,y) is in the second quadrant, the range of atan2(y,x) is [π / 2,π]; when the point (x,y) is in the third quadrant, the range of atan2(y,x) is [-π,-π / 2]; and when the point (x,y) is in the fourth quadrant, the range of atan2(y,x) is [-π / 2,0].

[0071]

[0072] Furthermore, to match the range of H, A must be multiplied by a coefficient of 2, so that φ = 2A, i.e.:

[0073]

[0074] S3, pseudo-color model mapping;

[0075] In this embodiment, the following formulas (10) to (12) are used.

[0076] H=φ (10)

[0077]

[0078]

[0079] Where Δ is the polarization robustness discrimination parameter. In this embodiment, if a certain region has good polarization robustness, i.e., low polarization noise, then we will... Mapping to V; if a certain region contains polarization noise, then we will still normalize the composite light intensity. Mapping to V. This highlights the low reflectivity, high polarization region while avoiding widespread pseudo-polarization.

[0080] S4. Polarization robustness determination;

[0081] In this embodiment, the following formulas (13) to (15) are used.

[0082]

[0083]

[0084] Δ = close(open(Δ,B),B) (15)

[0085] Where, f1 = sin 2 φ, f2=cos 2 φ, To perform neighborhood averaging with a template of 3*3 on f1. The smaller the g value, the better the polarization robustness at that point. t is the discrimination threshold, determined statistically through multiple sets of experiments. close and open are morphological opening and closing operations, and B is the structuring element with a size of 3*3.

[0086] S5, pseudo-color display.

[0087] In this embodiment, the image is converted from HSV color representation to RGB color representation for computer display and storage.

[0088] To further verify the effectiveness of the fusion method proposed in this patent, four metrics—standard deviation (SD), image entropy (En), average gradient (AG), and contrast (CR)—were used for evaluation. The proposed method was compared with the Wolff method and the IPQ method, as shown in Table 1. AG is the mean value of all points on the gradient map of an image, reflecting subtle detail contrasts and texture variations, as well as image sharpness. The calculation results show that the fusion results obtained using the proposed method exhibit improved values ​​for standard deviation (SD), image entropy (En), average gradient (AG), and contrast (CR). Therefore, the target contour is highlighted, and the target features are clearer.

[0089] Table 1. Objective evaluation data of fusion results from different methods

[0090]

[0091] In summary, this invention fully considers the physical meaning of polarization degree and polarization angle during the preprocessing of P and A, preserving as much multidimensional information of polarization parameters as possible to provide good data support for subsequent data processing. This invention addresses the tendency for dark regions in polarized images to have stronger polarization characteristics. Furthermore, by utilizing operations such as light intensity normalization and polarization data stretching, this invention makes the color features after pseudo-color fusion more pronounced than existing technologies. It improves upon the unsatisfactory pseudo-color fusion results caused by excessively dark background areas where the target is located. This invention determines whether a region is a region with strong polarization characteristics or a region with polarization noise, thereby highlighting the polarization characteristics of the target.

[0092] This invention addresses the characteristic that in practical application scenarios, the linear polarization degree of ground objects is mostly below 10%. It utilizes linear polarization degree stretching logic to stretch the linear polarization degree, thereby improving image contrast.

[0093] This invention addresses the issue that the value range of the arctan() function and the polarization angle processing results do not match the physical meaning of the polarization angle (the polarization angle's range is [0,π]). It replaces the arctan() function with the atan2() function, which more comprehensively considers the physical meaning of the polarization angle and improves the consistency between the fusion effect and the actual situation.

[0094] This invention utilizes the characteristics of the polarization angle to determine polarization robustness. Specifically, when the measurement robustness of the polarization degree relative to the linear Stokes parameter is poor, the polarization angle statistic approximates a Gaussian distribution; conversely, when the measurement robustness of the polarization degree relative to the linear Stokes parameter is good, the AOP (Average Polarization Angle) approximates a uniform distribution. Therefore, this characteristic of the polarization angle AOP can be used to estimate whether a certain region contains polarization noise. Based on the polarization robustness determination under dark backgrounds using the polarization angle characteristics, the pseudo-color fusion effect of images is further optimized.

[0095] This invention uses polarization robustness discrimination parameters to determine the polarization robustness and polarization noise of a specific region, which serves as a criterion for pseudo-color model mapping operations. This approach highlights regions with low reflectivity and high polarization characteristics while avoiding widespread pseudo-polarization phenomena.

[0096] This invention solves the technical problem in the prior art where the false color blending result is unsatisfactory due to the dark background area where the target is located.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pseudo-color fusion method for images against a dark background based on polarization angle characteristics, characterized in that, The method includes: S1. Using a polarization camera, collect the emitted light intensity of the target scene in at least three directions. Based on this, the Stokes parameter image data of the target scene is calculated using preset logic, and polarization information is obtained by parsing, wherein the polarization information includes: linear polarization degree. and polarization angle ; The emitted light intensity of the target scene is acquired using a polarization camera. The Stokes parameter image data is obtained through processing, and the emitted light intensity in three directions of the target scene is acquired using a polarization camera. , , Substitute into the formula: In order to obtain the synthesized light intensity Linear polarization parameters , ; The linear polarization degree is obtained analytically using the following logic. and the polarization angle : （4） （5）； S2, The synthesized light intensity I and the degree of linear polarization and the polarization angle These are respectively mapped to brightness in the HSV model. V saturation S、 tone H To obtain the pseudo-color model mapping applicable data, wherein the pseudo-color model mapping applicable data includes: normalized composite light intensity, linear polarization degree stretching data, polarization angle preprocessing data, and hue matching parameters; S3. Process the polarization angle using a preset polarization robustness discrimination logic. Characteristic data to obtain polarization robustness discrimination parameters; The polarization robustness determination is performed using the following logic to obtain the polarization robustness determination parameters. : （13） （14） （15） in, , , Corresponding image f Let M be a pixel in 1, and M be the number of pixels in its neighborhood S. Here, S is an eight-neighborhood, so M = 9. Indicates to Create template 3 3 neighborhood average, g These are the current point polarization robustness characterization parameters. t To determine the threshold, `close` and `open` are morphological opening and closing operations. B For structural elements; S4. Determine the state of polarization noise based on the polarization robustness discrimination parameter. When the polarization noise is less than a preset polarization noise threshold, take the maximum value of the normalized composite light intensity and the linear polarization degree stretching data to map it to the brightness. V When the polarization noise is greater than or equal to the preset polarization noise threshold, the normalized composite light intensity is... Mapped to the brightness V To obtain pseudo-color HSV images; The following logic is used for pseudo-color model mapping: （10） （11） （12） S5. Convert the pseudo-color HSV color representation to RGB color representation for display, so as to display the pseudo-color image on the computer.

2. The pseudo-color fusion method for images against a dark background based on polarization angle characteristics according to claim 1, characterized in that, In step S2, the linear polarization degree is determined using the following logic. Perform a stretching process to reduce the degree of linear polarization. Stretch to the applicable range of linear polarization degree values ​​to obtain the stretched linear polarization degree data. : （7）。 3. The pseudo-color fusion method for images against a dark background based on polarization angle characteristics according to claim 1, characterized in that, In step S2, the polarization angle is processed using the following logic. To obtain the polarization angle preprocessing data: （8）； The emitted light intensity is normalized using the following logic to transform its range to a suitable intensity range, thereby obtaining the normalized composite light intensity. : （6）； Using the following logic, the polarization angle is... Multiply by a preset coefficient to match the hue. H The value range is used to obtain the hue matching parameter. φ : （9）。 4. A pseudo-color fusion system for images against a dark background based on polarization angle characteristics, used to perform the pseudo-color fusion method for images against a dark background based on polarization angle characteristics as described in any one of claims 1 to 3, characterized in that, The system includes: The polarization information analysis module is used to acquire the emitted light intensity of the target scene in at least three directions using a polarization camera. Based on this, the Stokes parameter image data of the target scene is calculated using preset logic, and polarization information is obtained by parsing, wherein the polarization information includes: linear polarization degree. and polarization angle ; The data preprocessing module is used to process the synthesized light intensity. I and the degree of linear polarization and the polarization angle These are respectively mapped to brightness in the HSV model. V saturation S、 tone H To obtain pseudo-color model mapping applicable data, wherein the pseudo-color model mapping applicable data includes: normalized composite light intensity, linear polarization degree stretching data, polarization angle preprocessing data and hue matching parameters, and the data preprocessing module is connected to the polarization information parsing module; The polarization robustness discrimination module is used to process the polarization angle using preset polarization robustness discrimination logic. The characteristic data are used to obtain polarization robustness discrimination parameters. The polarization robustness discrimination module is connected to the data preprocessing module. The pseudo-color model mapping module is used to determine the state of polarization noise based on the polarization robustness discrimination parameter. When the polarization noise is less than a preset polarization noise threshold, the maximum value of the normalized composite light intensity and the linear polarization degree stretching data is taken and mapped to the brightness. V When the polarization noise is greater than or equal to the preset polarization noise threshold, the normalized composite light intensity is... Mapped to brightness V To obtain a pseudo-color HSV image, the module is connected to the data preprocessing module and the polarization robustness discrimination module. A pseudo-color display module is used to convert the pseudo-color HSV color representation into RGB color representation for displaying a pseudo-color image on a computer. The pseudo-color display module is connected to the pseudo-color model mapping module.

Images