HDR image processing method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of image processing, and discloses an HDR image processing method and device, electronic equipment and a storage medium, and the method comprises the steps: inputting a first RGB color image; converting the first RGB color image into a second RGB color image; converting the second RGB color image from an RGB color space into an HSV color space to obtain a V component image, an H component image and an S component image; performing Laplace transformation on the V component image to obtain an Lv component image; carrying out adaptive histogram equalization processing on the Lv component image to obtain a Dv component image; and based on the Dv component image, the H component image and the S component image, converting the second RGB color image from the HSV color space to an RGB color space to obtain a third RGB color image. According to the method, after the image is transformed, Laplace transformation and adaptive histogram equalization processing are carried out, details of the image are displayed more clearly, the brightness of the image is effectively improved, the brightness is uniform, and the image meets clinical requirements.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to an HDR image processing method, device, electronic device and storage medium. Background Art

[0002] Endoscope is a commonly used medical device with important clinical application value. The quality of its image affects the clinical use effect of the endoscope. Among the image processing enhancement methods, HDR (High Dynamic Range) processing method has a certain improvement effect on the image effect after color image processing. However, for the currently commonly used HDR processing algorithm, its algorithm complexity is high and its robustness is poor. It is not easy to process images in real time, and the processed images cannot meet the clinical diagnosis requirements. Summary of the invention

[0003] In view of this, the present invention provides an HDR image processing method, device, electronic device and storage medium to solve the problem that processed color images cannot meet clinical diagnosis requirements.

[0004] In a first aspect, the present invention provides an HDR image processing method, the method comprising:

[0005] Input a first RGB color image;

[0006] Converting the first RGB color image into a second RGB color image, wherein the image bit width of the second RGB color image is greater than the image bit width of the first RGB color image;

[0007] Convert the second RGB color image from the RGB color space to the HSV color space to obtain a V component image, an H component image, and an S component image;

[0008] The V component image is Laplace transformed to obtain L v component images;

[0009] For the L v The component images are processed by adaptive histogram equalization to obtain D v component images;

[0010] Based on the D v component image, the H component image and the S component image, convert the second RGB color image from the HSV color space to the RGB color space to obtain a third RGB color image.

[0011] Beneficial effect: After inputting the first RGB color image, the first RGB color image is converted into a second RGB color image with a larger image bit width to expand the dynamic range of the image and provide more space for subsequent processing. The second RGB color image is then converted from the RGB color space to the HSV color space to obtain a V component image, an H component image, and an S component image, so as to more conveniently process and analyze the color and brightness of the second RGB color image. The V component image is subjected to Laplace transform and adaptive histogram equalization to further improve the clarity and detail expression of the second RGB color image; finally, according to D v The second RGB color image is converted from the HSV color space to the RGB color space to obtain a third RGB color image, so as to enhance the details and clarity of the input RGB color image. By transforming the RGB color image and performing Laplace transform and adaptive histogram equalization processing, the details of the image are displayed more clearly, the complexity of image processing is effectively reduced, the brightness of the image is improved and the brightness is uniform, and the high requirements of clinical diagnosis on image quality are met.

[0012] In an optional implementation, the V component image is Laplace transformed to obtain L v Component images, including:

[0013] The V component image is convolved with the Laplace convolution kernel to obtain L v Component image.

[0014] In an optional implementation, the Laplace convolution kernel is

[0015] In an optional implementation, the calculation formula for converting the second RGB color image from the RGB color space to the HSV color space to obtain the V component image, the H component image, and the S component image is:

[0016] R ′ =R / 255;

[0017] G ′ =G / 255;

[0018] B ′ =B / 255;

[0019] C max =max(R ′ ,G ′ ,B ′ );

[0020] C min =min(R ′ ,G′ ,B ′ );

[0021] W=C max -C min ;

[0022] The H component image is:

[0023]

[0024] The S component image is:

[0025]

[0026] The V component image is:

[0027] V=C max

[0028] Among them, R ′ is the converted red channel value, G ′ is the converted green channel value, B ′ is the converted red channel value, R is the red channel value, G is the green channel value, B is the blue channel value, C is the blue channel value max is the maximum channel value, C min is the minimum channel value, W is the maximum distance of pixel value, H is the hue value of the image, S is the saturation value of the image, and V is the brightness value of the image.

[0029] In an optional embodiment, the D v component image, the H component image and the S component image, and the calculation formula for converting the second RGB color image from the HSV color space to the RGB color space is:

[0030]

[0031]

[0032] p=D v ×(1-S);

[0033] q=D v ×(1-f×S);

[0034] t=D v ×(1-(1-f)×S);

[0035]

[0036] Among them, h i , f, p, q, t are parameters, H is the hue value of the image, S is the saturation value of the image, D vis the brightness value of the transformed image, R is the red channel value, G is the green channel value, and B is the blue channel value.

[0037] In an optional implementation, after obtaining the third RGB color image, the method further includes:

[0038] The third RGB color image is converted into a fourth RGB color image, wherein the image bit width of the fourth RGB color image is smaller than the image bit width of the third RGB color image.

[0039] In a second aspect, the present invention further provides an HDR image processing device, comprising:

[0040] An input module, used for inputting a first RGB color image;

[0041] A first conversion module, used for converting the first RGB color image into a second RGB color image;

[0042] A second conversion module, used for converting the second RGB color image from the RGB color space to the HSV color space to obtain a V component image, an H component image and an S component image;

[0043] A calculation module is used to perform Laplace transform on the V component image to obtain L v component images;

[0044] A processing module for processing the L v The component images are processed by adaptive histogram equalization to obtain D v component images;

[0045] The third conversion module is used to convert the v component image, the H component image and the S component image, convert the second RGB color image from the HSV color space to the RGB color space to obtain a third RGB color image.

[0046] In an optional implementation, the operation module includes:

[0047] A convolution operation unit is used to perform a convolution operation on the V component image and the Laplace convolution kernel to obtain L v Component image.

[0048] In a third aspect, the present invention further provides an electronic device, comprising: a memory, a processor, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0049] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the above-mentioned HDR image processing method.

[0050] In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein at least one executable instruction is stored in the storage medium. When the executable instruction is executed on an electronic device / HDR image processing device, the electronic device / HDR image processing device executes the above-mentioned HDR image processing method.

[0051] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 It is a flowchart of an HDR image processing method provided by an embodiment of the present invention;

[0054] Figure 2 is an effect diagram of an image before processing in an embodiment of the present invention;

[0055] Figure 3 is an effect diagram of an image after processing in an embodiment of the present invention;

[0056] Figure 4 is a structural schematic diagram of an embodiment of an HDR image processing device provided by an embodiment of the present invention;

[0057] Figure 5 It is a schematic diagram of the structure of an embodiment of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0059] Related technologies, such as the multi-exposure fusion algorithm, can improve the clarity and details of images, but the algorithm complexity is high, resulting in slow processing speed and poor algorithm robustness, making it difficult to cope with diverse image processing needs. The HDR algorithm based on deep learning can improve the clarity and details of images, but the algorithm complexity is high, consumes a lot of resources, and is not easy to process images in real time; therefore, the image processing methods of related technologies cannot meet the requirements of clinical diagnosis.

[0060] In view of this, the present invention provides an HDR image processing method, device, electronic device and storage medium, which effectively reduce the complexity of image processing, improve the brightness of the image and make the brightness uniform, enhance the clarity and details of the image, and meet the high requirements of clinical diagnosis on image quality.

[0061] The following describes a specific embodiment of an HDR image processing method of the present invention. Figure 1 It is a flowchart of an HDR image processing method provided by an embodiment of the present invention. This specification provides method operation steps such as the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or server product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings (for example, in a parallel processor or multi-threaded processing environment). Specifically, Figure 1 As shown, the method may include the following steps:

[0062] Step S100: input a first RGB color image.

[0063] In the embodiment of the present invention, the RGB color image is composed of three color channels: red, green, and blue. The value range of each channel is usually 0 to 255. The image bit width of the input first RGB color image can be 8 bits. Compared with images with higher bit widths, the 8-bit image has a relatively small amount of data, is easy to store, transmit, and process, can improve data processing efficiency, and save storage space.

[0064] Step S200: convert the first RGB color image into a second RGB color image, wherein the image bit width of the second RGB color image is greater than the image bit width of the first RGB color image.

[0065] In the embodiment of the present invention, the image bit width of the second RGB color image may be 16 bits; the first RGB color image with an image bit width of 8 may be multiplied by 256 to obtain the second RGB color image with an image bit width of 16. In the first RGB color image with an image bit width of 8, each color channel uses 8 bits, i.e., 1 byte, to represent color information. Multiplying the first RGB color image by 256 is equivalent to shifting the value of each color channel left by 8 bits, i.e., converting the 8-bit color value into a 16-bit color value.

[0066] In a 16-bit RGB color image, each color channel uses 16 bits, or 2 bytes, to represent color information, and its value range is 0 to 65535. By multiplying an 8-bit RGB color image by 256, a 16-bit RGB color image can be obtained, in which the value range of each color channel is also correspondingly extended to 0 to 65535. When performing image processing operations, a higher bit width can reduce the loss of details caused by data truncation, avoid problems such as color overflow during image processing, ensure the accuracy of the processing results, and help improve the quality of the final image. Among them, the image bit widths of the first RGB color image and the second RGB color image are exemplary and are not specifically limited. In the actual processing process, it is sufficient to ensure that the image bit width of the first RGB color image is smaller than the image bit width of the second RGB color image.

[0067] Step S300: convert the second RGB color image from the RGB color space to the HSV color space to obtain a V component image, an H component image, and an S component image.

[0068] In the embodiment of the present invention, the HSV color space is composed of three components: hue, saturation, and value. Among them, the V component image represents the brightness of the image, that is, the brightness of the image; specifically, it represents the brightness of the pixels in the image, which corresponds to the brightness information in the RGB image. The H component image represents the hue, that is, the type of color; specifically, it represents the color in the image, such as red, green, blue, etc., and the value range is usually 0 to 360 degrees. The S component image represents the saturation, that is, the purity of the color; specifically, it represents the vividness of the color, and the value range is usually 0 to 1. By converting the RGB color space to the HSV color space, the color selection, adjustment, and matching of the image can be more intuitive, and the color and brightness of the image can be processed and analyzed more conveniently.

[0069] In one embodiment, the calculation formula of step S300 is:

[0070] First, you need to convert the R, G, and B values ​​to between 0 and 1:

[0071] R ′ =R / 255;

[0072] G ′ =G / 255;

[0073] B ′ =B / 255;

[0074] C max =max(R ′ ,G ′ ,B ′ );

[0075] C min =min(R ′ ,G ′ ,B ′ );

[0076] W=C max -C min ;

[0077] The H component image value is:

[0078]

[0079] The S component image value is:

[0080]

[0081] The V component image value is:

[0082] V=C max

[0083] Among them, R ′ is the converted red channel value, G ′ is the converted green channel value, B ′ is the converted red channel value, R is the red channel value, G is the green channel value, B is the blue channel value, C is the blue channel value max is the maximum channel value, C min is the minimum channel value, W is the maximum distance of pixel value, H is the hue value of the image, S is the saturation value of the image, and V is the brightness value of the image.

[0084] Step S400: Perform Laplace transform on the V component image to obtain L v Component image.

[0085] In the embodiment of the present invention, the V component image is convolved with the Laplacian convolution kernel to highlight the grayscale mutation area in the image. Further, the Laplacian convolution kernel is Multiply each pixel in the V component image with the Laplace convolution kernel and sum to calculate the new pixel value, and get L v Component image. L vThe component image emphasizes the regions with drastic gray-scale changes in the image, highlighting the edges and details of the image, significantly enhancing the image details and contrast, while requiring less computing resources; only transforming the V component will not change the color effect of the color image, ensuring the authenticity of the image color. Among them, the Laplacian convolution kernel is exemplary and not specifically limited, and can be set according to the usage requirements.

[0086] In other embodiments, the color image can also be converted from the RGB color space to the YCBCR color gamut, and the luminance component image therein is processed. Only processing the luminance component image of the color image and not processing the color component image can achieve the image processing effect and save computing resources.

[0087] Step S500, perform adaptive histogram equalization processing on the L v component image to obtain the D v component image.

[0088] In the embodiment of the present invention, the L v component image is divided into multiple non-overlapping square sub-regions; calculate the gray-scale histogram of each square sub-region; perform equalization processing on the gray-scale histogram of each square sub-region to make the luminance of each square sub-region uniform, thereby enhancing the contrast and clarity of the image; splice the gray-scale values of all square sub-regions according to the corresponding positions of the image before division to obtain the D v component image. Among them, the size r of the multiple square sub-regions is an adjustable parameter, and r is less than one-eighth of the short side of the L v component image to be processed, that is, r < min(H, L); H and L are the side lengths of the L v component image respectively, and min() represents the minimum value function. By performing adaptive histogram equalization processing on the L v component image, the contrast and clarity of the color image can be further improved.

[0089] Step S600, based on the D v component image, H component image and S component image, convert the second RGB color image from the HSV color space to the RGB color space to obtain the third RGB color image.

[0090] In the embodiment of the present invention, based on the D v component image, H component image and S component image, the calculation formula for converting the second RGB color image from the HSV color space to the RGB color space is:

[0091]

[0092]

[0093] p=D v ×(1-S);

[0094] q=D v ×(1-f×S);

[0095] t=D v ×(1-(1-f)×S);

[0096]

[0097] Among them, h i , f, p, q, t are parameters, H is the hue value of the image, S is the saturation value of the image, D v is the brightness value of the transformed image, R is the red channel value, G is the green channel value, and B is the blue channel value.

[0098] The HDR image processing method provided by the present invention converts the first RGB color image into a second RGB color image with a larger image bit width after inputting the first RGB color image, so as to expand the dynamic range of the image and provide a larger space for subsequent processing. The second RGB color image is then converted from the RGB color space to the HSV color space to obtain a V component image, an H component image, and an S component image, so as to more conveniently process and analyze the color and brightness of the second RGB color image. The V component image is subjected to Laplace transform and adaptive histogram equalization processing to further improve the clarity and detail expression of the second RGB color image; finally, according to the D v The second RGB color image is converted from the HSV color space to the RGB color space to obtain a third RGB color image, so as to enhance the details and clarity of the input RGB color image. By transforming the RGB color image and performing Laplace transform and adaptive histogram equalization processing, the details of the image are displayed more clearly, the complexity of image processing is effectively reduced, the brightness of the image is improved and the brightness is uniform, and the high requirements of clinical diagnosis on image quality are met.

[0099] like Figure 2 , Figure 3 As shown, Figure 2 After the picture in is processed by the HDR image processing method provided by the embodiment of the present invention, it becomes Figure 3 The image shown has significantly enhanced its brightness and clarity, and the details in the image are displayed more clearly.

[0100] In one embodiment, after step S600, the method further includes:

[0101] The third RGB color image is converted into a fourth RGB color image, and the image bit width of the fourth RGB color image is smaller than the image bit width of the third RGB color image.

[0102] In this embodiment, the image bit width of the third RGB color image can be 16 bits, and the image bit width of the fourth RGB color image can be 8 bits; the third RGB color image with an image bit width of 16 is divided by 256 to obtain the fourth RGB color image with an image bit width of 8. By reducing the image bit width, the storage space required for the color image can be reduced; a smaller image bit width can improve the transmission efficiency of the color image, and less computing resources and processing time are required when performing image processing. Among them, the image bit widths of the third RGB color image and the fourth RGB color image are exemplary and are not specifically limited. In the actual processing process, it is sufficient to ensure that the image bit width of the fourth RGB color image is smaller than the image bit width of the third RGB color image.

[0103] Second, as Figure 4 As shown, the present invention also provides an HDR image processing device, comprising:

[0104] The input module 100 is used to input a first RGB color image.

[0105] The first conversion module 200 is used to convert the first RGB color image into a second RGB color image.

[0106] The second conversion module 300 is used to convert the second RGB color image from the RGB color space to the HSV color space to obtain a V component image, an H component image and an S component image; the calculation formula is:

[0107] R ′ =R / 255;

[0108] G ′ =G / 255;

[0109] B ′ =B / 255;

[0110] C max =max(R ′ ,G ′ ,B ′ );

[0111] C min =min(R ′ ,G ′ ,B ′ );

[0112] W=C max -C min ;

[0113] The H component image is:

[0114]

[0115] The S component image is:

[0116]

[0117] The V component image is:

[0118] V=C max

[0119] Among them, R ′ is the converted red channel value, G ′ is the converted green channel value, B ′ is the converted red channel value, R is the red channel value, G is the green channel value, B is the blue channel value, C is the blue channel value max is the maximum channel value, C min is the minimum channel value, W is the maximum distance of pixel value, H is the hue value of the image, S is the saturation value of the image, and V is the brightness value of the image.

[0120] The operation module 400 is used to perform Laplace transform on the V component image to obtain L v component images;

[0121] The processing module 500 is used to v The component images are processed by adaptive histogram equalization to obtain D v component images;

[0122] The third conversion module 600 is used to convert the v component image, H component image and S component image, convert the second RGB color image from the HSV color space to the RGB color space to obtain a third RGB color image; the calculation formula is:

[0123]

[0124]

[0125] p=D v ×(1-S);

[0126] q=D v ×(1-f×S);

[0127] t=D v ×(1-(1-f)×S);

[0128]

[0129] Among them, hi , f, p, q, t are parameters, H is the hue value of the image, S is the saturation value of the image, D v is the brightness value of the transformed image, R is the red channel value, G is the green channel value, and B is the blue channel value.

[0130] In one embodiment, the computing module 400 includes:

[0131] The convolution operation unit is used to convolve the V component image with the Laplace convolution kernel to obtain L v component image; the Laplacian convolution kernel is

[0132] In one embodiment, it also includes:

[0133] The fourth conversion module is used to convert the third RGB color image into a fourth RGB color image, wherein the image bit width of the fourth RGB color image is smaller than the image bit width of the third RGB color image.

[0134] The device and method embodiments in the embodiments of this application are based on the same application concept.

[0135] In a third aspect, the present invention further provides an electronic device, comprising: a memory, a processor, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the above-mentioned HDR image processing method.

[0136] like Figure 5 As shown, the electronic device may include: a processor (processor) 502 , a communication interface (Communications Interface) 504 , a memory (memory) 506 , and a communication bus 508 .

[0137] The processor 502, the communication interface 504, and the memory 506 communicate with each other via a communication bus 508. The communication interface 504 is used to communicate with other devices such as a client or other server network elements. The processor 502 is used to execute a program 510, which can specifically execute the relevant steps in the above-mentioned embodiment of the HDR image processing method.

[0138] Specifically, the program 510 may include program code including computer executable instructions.

[0139] The processor 502 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the electronic device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0140] The memory 506 is used to store the program 510. The memory 506 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0141] The program 510 may be specifically called by the processor 502 to enable the electronic device to execute the relevant steps in the above-mentioned embodiment of the HDR image processing method.

[0142] It can be understood by those skilled in the art that Figure 5 The structure shown is only for illustration and does not limit the structure of the above-mentioned device. Figure 5 More or fewer components as shown, or with Figure 5 Different configurations are shown.

[0143] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, in which at least one executable instruction is stored. When the executable instruction is executed on an electronic device / HDR image processing device, the electronic device / HDR image processing device executes the above-mentioned HDR image processing method.

[0144] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present invention are not directed to any particular programming language.

[0145] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself is a separate embodiment of the present invention.

[0146] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0147] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A HDR image processing method, characterized in that: The method comprises: Input a first RGB color image; Converting the first RGB color image into a second RGB color image, wherein the image bit width of the second RGB color image is greater than the image bit width of the first RGB color image; Convert the second RGB color image from the RGB color space to the HSV color space to obtain a V component image, an H component image, and an S component image; The V component image is Laplace transformed to obtain L v component images; For the L v The component images are processed by adaptive histogram equalization to obtain D v component images; Based on the D v component image, the H component image and the S component image, convert the second RGB color image from the HSV color space to the RGB color space to obtain a third RGB color image.

2. The HDR image processing method according to claim 1, characterized in that: The V component image is Laplace transformed to obtain L v Component images, including: The V component image is convolved with the Laplace convolution kernel to obtain L v Component image.

3. The HDR image processing method according to claim 2, characterized in that: The Laplacian convolution kernel is 4. The HDR image processing method according to claim 1, characterized in that: The calculation formula for converting the second RGB color image from the RGB color space to the HSV color space to obtain the V component image, the H component image and the S component image is: R ′ =R / 255; G ′ =G / 255; B ′ =B / 255; C max =max(R ′ ,G ′ ,B ′ ); C min =min(R ′ ,G ′ ,B ′ ); W=C max -C min ; The H component image is: The S component image is: The V component image is: V=C max Among them, R ′ is the converted red channel value, G ′ is the converted green channel value, B ′ is the converted red channel value, R is the red channel value, G is the green channel value, B is the blue channel value, C is the blue channel value max is the maximum channel value, C min is the minimum channel value, W is the maximum distance of pixel value, H is the hue value of the image, S is the saturation value of the image, and V is the brightness value of the image.

5. The HDR image processing method according to claim 1, characterized in that: Based on the D v component image, the H component image and the S component image, and the calculation formula for converting the second RGB color image from the HSV color space to the RGB color space is: p=D v ×(1-S); q=D v ×(1-f×S); t=D v ×(1-(1-f)×S); Among them, h i , f, p, q, t are parameters, H is the hue value of the image, S is the saturation value of the image, D v is the brightness value of the transformed image, R is the red channel value, G is the green channel value, and B is the blue channel value.

6. The HDR image processing method according to claim 1, characterized in that: After obtaining the third RGB color image, the method further includes: The third RGB color image is converted into a fourth RGB color image, wherein the image bit width of the fourth RGB color image is smaller than the image bit width of the third RGB color image.

7. An HDR image processing device, characterized in that: include: An input module, used for inputting a first RGB color image; A first conversion module, used for converting the first RGB color image into a second RGB color image; A second conversion module, used for converting the second RGB color image from the RGB color space to the HSV color space to obtain a V component image, an H component image and an S component image; A calculation module is used to perform Laplace transform on the V component image to obtain L v component images; A processing module for processing the L v The component images are processed by adaptive histogram equalization to obtain D v component images; The third conversion module is used to convert the v component image, the H component image and the S component image, convert the second RGB color image from the HSV color space to the RGB color space to obtain a third RGB color image.

8. The HDR image processing device according to claim 7, characterized in that: The operation module comprises: A convolution operation unit is used to perform a convolution operation on the V component image and the Laplace convolution kernel to obtain L v Component image.

9. An electronic device, characterized in that: include: A memory, a processor, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the HDR image processing method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one executable instruction, and when the executable instruction is executed on the electronic device / HDR image processing device, the electronic device / HDR image processing device executes the HDR image processing method according to any one of claims 1 to 6.

Images