Underwater optical image enhancement method and system

Abstract

The invention belongs to the technical field of underwater optical image processing, and discloses an underwater optical image enhancement method and system. According to the method, the color attenuation degree of an underwater optical image is evaluated, a dynamic compensation factor is calculated, and the color of the underwater image is corrected by utilizing the pixel distribution standard deviation between different color channels according to the histogram distribution characteristics of image pixels; step-by-step positioning is carried out on the basis of the image color corrected by the underwater image color through recursion iteration by using a block index strategy until a set threshold value is met, and a background light estimation value is finally determined; and completing the enhancement of the underwater image based on the obtained background light estimation value. According to the method, a more accurate background light estimation value is finally determined, so that good image defogging performance is achieved; and the visual quality of the underwater image is greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of underwater optical image processing, and in particular relates to an underwater optical image enhancement method and system. Background Art

[0002] Rivers, lakes and seas occupy a core position in the ecosystem, providing living space for countless aquatic organisms, plants and animals, and also playing a key role in ecological processes such as the global carbon cycle and water cycle. In recent years, the rapid development of human society has intensified the demand for underwater environmental detection. In the process of using modern technical means to detect underwater scenes, acoustics and optics are the two mainstream means. Compared with acoustic data, underwater optical images are favored due to their high resolution and rich color information. With the rapid development of marine resource development and underwater detection technology, underwater optical imaging systems play an irreplaceable role in marine ecological monitoring (coverage rate of 82%), underwater engineering inspection (average annual application growth of 37%) and other fields. In this context, how to improve the visibility of underwater optical images has become one of the frontier issues that need to be solved urgently at home and abroad.

[0003] However, the absorption and scattering effect of water medium on light causes serious quality degradation of the acquired images. The selective absorption of light of different wavelengths by water causes significant color deviation of the image. The attenuation rate of the red light band at a depth of 5 meters has reached more than 80%, resulting in serious distortion of the image color. At the same time, the backscattering effect caused by suspended particles in the water forms a fogging phenomenon, which causes the image contrast to drop by more than 60%, and the detailed texture is blurred, which directly affects the visual recognition accuracy and operation reliability of underwater equipment.

[0004] The applicability of traditional underwater image enhancement methods is seriously impaired in the face of diverse underwater optical data sets. Traditional physical models usually assume that the optical parameters of water bodies are fixed values, but the spatial variation of parameters such as turbidity and depth in actual environments can lead to model mismatch, especially in near-shore turbid waters, where the attenuation coefficient of the blue-green channel can fluctuate by more than 20%, causing color discontinuity in the corrected image. Some researchers have proposed a method based on global and local equalization of histograms and multi-scale fusion of dual images to improve the quality of images in the real world and low light; however, for underwater images taken by different underwater dedicated cameras in the same scene, the enhanced images cannot achieve consistency in background color, and do not involve images taken at turbidity levels (Bai et al, 2020). In terms of image dehazing, the applicability of traditional dark channel prior methods in underwater scenes is severely limited. The widespread use of artificial light sources has led to a lack of true dark pixels in images, and about 60% of underwater images cannot accurately estimate the background light intensity using traditional methods. Based on this, the underwater dark channel prior came into being. By eliminating the red channel that is greatly affected by the underwater environment, only the data of the blue and green channels are used as input to complete the underwater image defogging task (Drews et al, 2013). This method often misestimates the background light when there are bright objects in the image. Based on the current technical status, it is urgent to develop new enhancement methods to break through the following bottlenecks: establish a color compensation mechanism that dynamically adapts to changes in water quality parameters to solve the color deviation problem of traditional methods in complex environments; design an estimation model that can distinguish between real background light and interference reflections to improve the accuracy of eliminating the fog effect.

[0005] Traditional underwater image enhancement methods often focus on the optimization of enhancement algorithms, but lack comprehensive mechanism analysis such as tracing and grading of different underwater degraded images, which limits their applicability in diverse underwater scenes.

[0006] Through the above analysis, the problems and defects of the existing technology are as follows: the existing adaptive method adopts a fixed threshold compensation strategy, which is prone to over-compensation when the scene lighting conditions suddenly change. For example, when strong artificial lighting intervenes, it will cause loss of details in the highlight area. Although the deep learning solution performs well on specific data sets, its color mapping relationship deviates from the real physical process. When the water type changes, systematic color deviation will occur, which seriously affects the color consistency of cross-water equipment. Although the improved solution based on dark channel prior improves the estimation accuracy of some scenes through a priority strategy, it still produces significant deviations in areas containing metal reflectors or bioluminescence, resulting in local overexposure or artifacts in the dehazed image. Summary of the invention

[0007] To overcome the problems existing in the related technologies, the disclosed embodiments of the present invention provide an underwater optical image enhancement method and system, specifically an underwater optical image enhancement method based on dynamic color compensation and refined background light estimation.

[0008] The technical solution is as follows: An underwater optical image enhancement method includes the following steps: S1, perform underwater image color correction based on a dynamic compensation factor. By evaluating the color attenuation degree of the underwater optical image, calculate the dynamic compensation factor; for the histogram distribution characteristics of image pixels, use the pixel distribution standard deviation between different color channels to correct the color of the underwater image. S2, based on the color of the image after color correction of the underwater image, use a block indexing strategy. Through recursive iteration, gradually locate until the set threshold is met to determine the background light estimation value. S3, based on the obtained background light estimation value, complete the enhancement of the underwater image.

[0009] Further, calculating the dynamic compensation factor includes: According to the gray world assumption, for a normally colored image, the average values of each color channel tend to be the same value. For the color attenuation of the underwater image, divide the color attenuation degree into three levels, including mild, moderate, and severe; define the dynamic compensation factor, and perform targeted color compensation on the image according to different color attenuation degrees. In the targeted color compensation, the color offset value is obtained as follows: ; In the formula, is the image before color compensation, is the mean value of

[0010] Further, for the histogram distribution characteristics between color channels, use the ratio of standard deviations to optimize the color compensation process for the position of pixels in the image. The color correction method is expressed as: ; In the formula, is the image after preliminary color compensation, is the color channel with the largest pixel mean value, is the position in the image, is the standard deviation solution, is the dynamic compensation factor, which adjusts its own value according to different color attenuation degrees; is the position of the pixel in the image; is the mean value of , is the red channel, is the green channel, is the blue channel; The position of pixels in the image The process of performing color compensation includes: Normalize the pixel values of each channel to the interval [0,1]; for the position of pixels in the image The process of performing color compensation is expressed as: .

[0011] In step S2, determining the background light estimation value includes: S201, input the color-corrected underwater image; S202, obtain the dark channel image; S203, execute the block indexing strategy: divide the dark channel image into four sub-images of equal size; perform score statistics on the divided sub-images; S204, by continuously iterating on the sub-region with the highest score, select the sub-image with the highest score as the background light candidate region until the size of the sub-region meets the set threshold; if it meets the set threshold, execute step S205; S205, map this region to the color-corrected underwater input image, and select the maximum pixel value of the input image as the background light estimation value.

[0012] In step S203, the calculation formula for the score is: ; In the formula, is the score of this region, is the divided sub-region, is to solve the mean value, is to solve the standard deviation.

[0013] In step S204, if it does not meet the set threshold, return to step S203.

[0014] In step S205, the maximum pixel value of the input image is the point with the highest brightness.

[0015] Another object of the present invention is to provide an underwater optical image enhancement system, which implements the underwater optical image enhancement method, and the system includes: An underwater image color correction module for performing color correction on an underwater image based on a dynamic compensation factor, calculating the dynamic compensation factor by evaluating the color attenuation degree of the underwater optical image, and correcting the color of the underwater image according to the histogram distribution characteristics of the image pixels and using the standard deviation of pixel distribution between different color channels; A background light estimation value determination module, which is used to determine the background light estimation value by gradually positioning through recursive iteration based on the image color after correcting the underwater image color using a block indexing strategy until a set threshold is met. An underwater image enhancement module, which is used to complete the enhancement of the underwater image based on the obtained background light estimation value.

[0016] Furthermore, the underwater optical image enhancement system is carried on a computer-readable storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the functions in the above underwater optical image enhancement system can be realized.

[0017] Furthermore, the application of the underwater optical image enhancement system in an underwater optical imaging system for marine ecological monitoring and underwater engineering detection.

[0018] Combining all the above technical solutions, the beneficial effects of the present invention are as follows: In view of the complexity and heterogeneity of the underwater optical image imaging environment, the present invention proposes a foggy image enhancement strategy based on dynamic color compensation and refined background light estimation. First, a dynamic compensation color correction algorithm is proposed. This algorithm evaluates the color attenuation degree of the underwater optical image, calculates the dynamic compensation factor, and considering the histogram distribution characteristics of the image pixels, uses the pixel distribution standard deviation between different color channels to correct the image color, providing a more realistic image color for the next step of image defogging. Secondly, a refined background light estimation strategy is proposed. This method uses a block indexing strategy, through recursive iteration, gradually locates until a set threshold is met, and finally determines a more accurate background light estimation value to achieve good image defogging performance. The above two steps are used to greatly improve the visual quality of the underwater image.

[0019] The present invention can effectively contribute to the improvement of the quality of underwater images. By analyzing the degradation characteristics of underwater images, the present invention creatively classifies the color attenuation of underwater images and defines the calculation formula of the color deviation value. The present invention also proposes an underwater image color correction algorithm based on dynamic compensation. Through the creative design of the dynamic factor, it realizes effective compensation for different degrees of color attenuation of underwater images, significantly improving the color correction effect of underwater images. The present invention also uses a block indexing strategy to achieve accurate background light indexing, thereby obtaining a more accurate transmittance, and combining with the dark channel prior, achieving a good underwater image defogging effect. Description of the Drawings

[0020] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure; Figure 1It is the flowchart of the underwater optical image enhancement method provided by the embodiments of the present invention; Figure 2 It is the color attenuation diagram of a certain underwater fish in different degrees provided by the embodiments of the present invention; wherein, Figure 2 Figure (a) in it is an image of an underwater fish, Figure 2 Figure (b) in it is the pixel distribution diagram of the image of an underwater fish; Figure 3 It is the color attenuation diagram of a certain underwater fish two in different degrees provided by the embodiments of the present invention; wherein, Figure 3 Figure (a) in it is an image of an underwater fish two, Figure 3 Figure (b) in it is the pixel distribution diagram of the image of an underwater fish two; Figure 4 It is the color attenuation diagram of different degrees in a certain underwater diving activity provided by the embodiments of the present invention; wherein, Figure 4 Figure (a) in it is an image in the underwater diving activity, Figure 4 Figure (b) in it is the pixel distribution diagram of the image in the underwater diving activity; Figure 5 It is the flowchart of determining the background light estimation value provided by the embodiments of the present invention; Figure 6 It is the effect diagram of the color correction result of the images with different degradation degrees of the present invention; Figure 7 It is the comparison schematic diagram between the present invention and the mainstream methods. Detailed implementation manners

[0021] To make the above objects, features and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention in conjunction with the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific implementations disclosed below.

[0022] For the most common type of fogging blur degradation, the present invention proposes an enhancement strategy based on dynamic color compensation and refined background light estimation. Through the design of the dynamic compensation factor, effective compensation for color attenuation in different degrees is achieved. On this basis, a block indexing strategy is used to obtain a more accurate background light estimation value, and the dark channel prior is combined to solve the image fogging problem.

[0023] Embodiment 1, as Figure 1 shown, the underwater optical image enhancement method provided by the embodiments of the present invention includes: S1. Perform underwater image color correction based on a dynamic compensation factor. Calculate the dynamic compensation factor by evaluating the color attenuation degree of the underwater optical image. For the histogram distribution characteristics of the image pixels, use the standard deviation of the pixel distribution between different color channels to correct the color of the underwater image; Provide the true image color for image defogging in the next background light estimation; Exemplarily, previous methods perform well in shallow water areas or environments with turbid water quality. Because in these cases, the green channel can retain more complete information compared to the red and blue channels. However, when the environment for obtaining the underwater image changes (obtaining images in waters with better water quality and greater depth), the pixel values of the green channel are generally low, while the relative attenuation of the blue channel is less. At this time, this method cannot achieve effective color correction. To solve this problem, the present invention proposes an improved underwater image color correction method. In the conventional technology, the pixel values of each channel are normalized to the interval [0, 1]. The process of color compensation for the pixels at position x in the image can be expressed as: (1) In the formula, is the image after preliminary color compensation, is the color channel with the largest pixel mean, is the position in the image, is to solve the standard deviation, is the dynamic compensation factor, which adjusts its own value according to different color attenuation degrees; is the position of the pixel in the image; is the mean value of , is the red channel, is the green channel, is the blue channel; is the image before color compensation, is the mean value of

[0024] In practice, the present invention has found that when the color attenuation of the underwater image is relatively severe, the method of formula (1) cannot achieve the ideal enhancement effect. Through analysis, the main reasons for the existing technology are as follows: (1) In the absorption and scattering effects of light by the water medium, the attenuation degree of visible light underwater increases significantly with the increase of the propagation distance. After reaching a certain depth, the information loss of the underwater image becomes very serious. At this time, relying solely on the single metric value in formula (1) is difficult to accurately measure the color attenuation degree of the image.

[0025] (2) The conventional underwater imaging model (Equation (4)) does not consider the distribution characteristics of pixel values and only performs color correction based on the differences between the mean values of color channels. This method is obviously insufficient for complex color attenuation problems and is difficult to comprehensively reflect the actual degradation of the image.

[0026] According to the gray world assumption, for a normally colored image, the average values of its color channels should tend to the same value. Based on this theory, for the color attenuation problem of underwater images, the present invention divides the degree of color attenuation into three levels: mild, moderate, and severe. To this end, a dynamic compensation factor is defined to perform targeted color compensation on the image according to different degrees of color attenuation. The definition of the dynamic compensation factor refers to Figure 2 the color attenuation diagrams of a certain underwater fish at different levels (the pixel distribution diagram of the image with mild color attenuation, where Figure 2 Figure (a) in Figure 2 is the image of a certain underwater fish, Figure 3 the color attenuation diagrams of a certain underwater fish two at different levels (the pixel distribution diagram of the image with moderate color attenuation, where Figure 3 Figure (a) in Figure 3 is the image of a certain underwater fish two, Figure 4 and the color attenuation diagrams of a certain underwater diving activity at different levels (the pixel distribution diagram of the image with severe color attenuation, where Figure 4 Figure (a) in Figure 4 is the image of the underwater diving activity, Table 1 The definition of the dynamic compensation factor refers to

[0027] The present invention innovatively proposes that the color deviation value is obtained as follows: (2); In the formula, is the image before color compensation, is the mean value of

[0028] At the same time, considering the histogram distribution characteristics between color channels, the ratio of standard deviations is used to further optimize Equation (1). The present invention innovatively proposes that the finally proposed color correction method is expressed as: ; In the formula, is the image after preliminary color compensation, is the color channel with the largest pixel mean, is the image before color compensation, is the mean value of, is the position in the image the final enhancement result of, is to solve the standard deviation, is the dynamic compensation factor, which adjusts its own value according to different degrees of color attenuation; is the position of the pixel in the image; is the mean value of, , is the red channel, is the green channel, is the blue channel; the position of the pixel in the image The process of color compensation for the pixel position in the image includes: Although color correction is an indispensable step in underwater image enhancement, it cannot solve the problem of fogging blur caused by the scattering effect. This is because the scattering effect will cause the loss of edge and detail information. Therefore, the present invention subsequently proposes a method that can obtain a more accurate background light estimation value for defogging underwater images.

[0029] S2. Based on the color of the image after color correction of the underwater image, using the block indexing strategy, through recursive iteration, gradually locate until the set threshold is met to determine the background light estimation value; Finally, a more accurate background light estimation value is determined to achieve good image defogging performance.

[0030] Exemplarily, according to the Jaffe-McGlamey underwater optical imaging model, the information obtained by the optical image acquisition device mainly consists of three parts: (1) Direct component (DC): The part of the light directly reflected from the object surface to the camera; (2) Forward scattering component (FSC): The part of the light that deviates from the original direction and finally reaches the camera after scattering; (3) Backscattering component (BSC): The part of the light that interacts with the water particles and scatters before being reflected to the camera. Generally, due to the small distance between the object and the camera, the influence caused by the forward scattering component (FSC) can be ignored. The underwater imaging model can be simplified as: (4) In the formula, is the direct component (DC), is the backscattering component (BSC), are the color channel intensity values of the underwater image captured by the camera and the non-degraded underwater image at the image position respectively, is the uniform background light, is the medium transmittance, and the expression is: (5) In the formula, is the distance between the target scene and the camera, is the attenuation coefficient of the target scene.

[0031] Dark channel prior is a priori knowledge obtained based on the statistics of outdoor fog-free images. In the local areas of most non-sky regions, some pixels usually exhibit extremely low intensities in at least one color channel (RGB). The present invention has collected and analyzed a large number of outdoor images. By establishing a histogram, it can be seen that approximately 75% of the pixel intensities in the dark channel are zero, and 90% of the pixel intensities are lower than 25. These statistical results provide strong theoretical support for the dark channel prior hypothesis. The dark channel is defined as: (6) In the formula, is the dark channel image at the pixel of , is the local pixel block, such as 3*3, 5*5 in size; is one of the three color channels, is the pixel within the region of one of the three color channels , is the three channels in the RGB image, is red, is green, is blue; is the local pixel block centered on the pixel at the position.

[0032] By transforming formula (4), we can obtain: ; Within each local window the transmittance is a constant . By performing the minimum operation twice on both sides of the above formula, we get: ; According to the dark channel prior theory and formula (6), we know that: ; That is: ; Considering formula (4) and formula (6), in order to obtain a more accurate background light, the present invention uses the image captured by the camera and the background light to estimate the transmittance , the expression is: (7) Considering that when the transmittance is very small, it will cause the pixel value of the restored image to be too large, resulting in the overall image transitioning to the white field. Generally, a threshold is set for constraint. The formula for the final image defogging is as follows: (8) As can be seen from formula (8), as long as the present invention can obtain accurate background light and transmittance it can restore a clear image. However, directly applying the dark channel prior to the defogging process of underwater optical images does not achieve ideal results.

[0033] Therefore, the present invention proposes a process for determining the estimated value of the background light, as Figure 5 shown. The refined background light estimation of the present invention is based on the underwater image after color correction. After color correction, the color deviation of the underwater image is restored to a certain extent. At this time, the phenomenon of fogging and blurring is closer to the state of outdoor foggy images, and the advantage of the dark channel prior can be better exerted. In the past, when there were over-bright non-background light objects in the image, the background light was often misestimated, which in turn affected the estimated value of the transmittance. To address this problem, the present invention utilizes the characteristics of continuous background light regions with relatively high pixel values and adopts a block search method to estimate the background light region.

[0034] The determination of the estimated value of the background light includes: S201, input the underwater image after color correction; S202, obtain the dark channel image; S203, execute the block indexing strategy: divide the dark channel image into four sub-images of equal size; perform score statistics on the divided sub-images; For the score statistics of the divided sub-images, the present invention innovatively proposes that the formula for calculating the score is: (9) In the formula, is the score of this region, is the divided sub-region, is to solve the mean value, is to solve the standard deviation.

[0035] S204. By continuously iterating on the sub-region with the highest score, select the sub-image with the highest score as the candidate background light region until the size of the sub-region meets the threshold set in the present invention (the size of the sub-image after segmentation, the threshold is the size (dimension) of the sub-image after segmentation, such as 32*32, and stop iterating when it is smaller than this size); if it does not meet the threshold set in the present invention, return to step S203; if it meets the threshold set in the present invention, execute step S205; S205. Map this region to the color-corrected underwater input image, and select the maximum pixel value of the input image as the estimated background light value.

[0036] S3. Based on the obtained estimated background light value, complete the enhancement of the underwater image.

[0037] As can be seen from the above embodiments, the present invention calculates the dynamic compensation factor by evaluating the color attenuation degree of the underwater optical image through the color correction algorithm. Considering the histogram distribution characteristics of the image pixels, the color restoration is constrained by using the standard deviation of the pixel distribution between different color channels. Using the block indexing strategy, through recursive iteration, gradually locate the region where the background light is located; the present invention solves the problem of inaccurate image color correction; solves the problem of incomplete color restoration of images with different degrees of color attenuation; and improves the accuracy of background light estimation by combining block indexing.

[0038] Embodiment 2. The present invention provides an underwater optical image enhancement system, which includes: An underwater image color correction module, which is used to perform underwater image color correction based on the dynamic compensation factor, calculate the dynamic compensation factor by evaluating the color attenuation degree of the underwater optical image, and correct the color of the underwater image by using the standard deviation of the pixel distribution between different color channels for the histogram distribution characteristics of the image pixels; A background light estimated value determination module, which is used to, based on the color of the image after the underwater image color correction, use the block indexing strategy, through recursive iteration, gradually locate until the set threshold is met, and finally determine the estimated background light value; An underwater image enhancement module, which is used to complete the enhancement of the underwater image based on the obtained estimated background light value.

[0039] To further illustrate the relevant effects of the embodiments of the present invention, the following experiments are carried out.

[0040] Color correction effect verification: The present invention selects underwater optical images containing a standard 24-color card for verifying the color restoration degree. The comparison results with the standard 24-color card show that the method of the present invention can achieve satisfactory effects, and the colors on the color card are accurately restored. Each picture in the image dataset used in the present invention is taken with a different camera, which indicates that the color correction method proposed by the present invention is not affected by camera parameters.

[0041] Dynamic compensation for different degradation degrees: As Figure 6 As shown in the color correction result effect diagrams of images with different degradation degrees, the present invention selects three underwater images with different degradation degrees and conducts a comparative experiment with a classical method. The experimental results show that for images with relatively severe color attenuation, the method of the present invention is significantly superior to the classical method in terms of color correction effect.

[0042] After color correction and image dehazing of the image, the present invention obtains the complete process of clear underwater optical images, verifies the effectiveness of the proposed underwater image enhancement method, and compares it with representative methods. The images used in the experiments of the present invention are all based on publicly available real underwater optical image datasets on the Internet (UIEB, EUVP, OceanDark, etc.).

[0043] Figure 7 This is a comparison schematic between the present invention and mainstream methods. The last column is the result diagram of the present invention; the experiment shows that in image dehazing of the present invention, the optimization of background light estimation based on the block indexing strategy is carried out on the color-corrected image. During the iterative process, the characteristic that the pixel intensity in the background light area is relatively high ensures that the sub-image containing the background light has a relatively large pixel average value. At the same time, the continuity of the background light distribution ensures that the standard deviation of the sub-image where the background light is located is relatively small.

[0044] As mentioned above, the above is only a relatively preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. An underwater optical image enhancement method, characterized in that: The method comprises the following steps: S1, performs underwater image color correction based on dynamic compensation factors, calculates the dynamic compensation factors by evaluating the color attenuation of underwater optical images; and corrects the underwater image color by using the standard deviation of pixel distribution between different color channels based on the histogram distribution characteristics of image pixels; S2, based on the image color corrected by the underwater image color, using the block indexing strategy, through recursive iteration, gradually locate until the set threshold is met, and determine the background light estimation value; S3, based on the obtained background light estimation value, the underwater image is enhanced.

2. The underwater optical image enhancement method according to claim 1, characterized in that: In step S1, the dynamic compensation factor is calculated, including: according to the grayscale world assumption, for a normal color image, the average values ​​of each color channel tend to the same value, and for the color attenuation of the underwater image, the color attenuation degree is divided into three levels, including mild, moderate and severe; the dynamic compensation factor is defined, and the image is subjected to targeted color compensation according to different color attenuation degrees. In the targeted color compensation, the color deviation value The acquisition formula is as follows: ； In the formula, is the image before color compensation, for The mean of .

3. The underwater optical image enhancement method according to claim 2, characterized in that: According to the histogram distribution characteristics between color channels, the ratio of standard deviation is used to compare the position of pixels in the image. The color compensation process is optimized and the color correction method is expressed as: ； In the formula, is the image after preliminary color compensation, is the color channel with the largest pixel mean, is the position in the image The final enhancement result is To solve for the standard deviation, It is a dynamic compensation factor, which adjusts its value according to the degree of color attenuation; is the position of the pixel in the image; for The mean of , is the red channel, For the green channel, is the blue channel; The location of the pixel in the image The color compensation process includes: Normalize the pixel value of each channel to the interval [0,1]; The process of color compensation is expressed as: 。 4. The underwater optical image enhancement method according to claim 1, characterized in that: In step S2, determining a background light estimation value includes: S201, inputting a color-corrected underwater image; S202, acquiring a dark channel image; S203, executing a block indexing strategy: dividing the dark channel image into four sub-images of equal size; and performing score statistics on the divided sub-images; S204, by continuously iterating the sub-region with the highest score, selecting the sub-image with the highest score as the background light candidate region, until the size of the sub-region meets the set threshold; if it meets the set threshold, executing step S205; S205, mapping the area to the underwater input image after color correction, and selecting the maximum pixel value of the input image as the background light estimation value.

5. The underwater optical image enhancement method according to claim 4, characterized in that: In step S203, the score calculation formula is: ； In the formula, is the score for this area, is the segmented sub-region, To find the mean, To solve for the standard deviation.

6. The underwater optical image enhancement method according to claim 4, characterized in that: In step S204, if it does not meet the set threshold, return to step S203.

7. The underwater optical image enhancement method according to claim 4, characterized in that: In step S205, the maximum pixel value of the input image is the point with the highest brightness.

8. An underwater optical image enhancement system, characterized in that: The system implements the underwater optical image enhancement method according to any one of claims 1 to 7, and the system comprises: The underwater image color correction module is used to perform underwater image color correction based on the dynamic compensation factor. The dynamic compensation factor is calculated by evaluating the color attenuation degree of the underwater optical image, and the color of the underwater image is corrected by using the standard deviation of the pixel distribution between different color channels according to the histogram distribution characteristics of the image pixels. A background light estimation value determination module is used to determine the background light estimation value based on the image color corrected by the underwater image color, using a block indexing strategy, through recursive iteration, gradually locating until a set threshold is met; The underwater image enhancement module is used to enhance the underwater image based on the obtained background light estimation value.

9. The underwater optical image enhancement system according to claim 8, characterized in that: The underwater optical image enhancement system is mounted on a computer-readable storage medium, and the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the functions of the underwater optical image enhancement system can be realized.

10. The underwater optical image enhancement system according to claim 8, characterized in that: The underwater optical image enhancement system is applied to underwater optical imaging systems for marine ecological monitoring and underwater engineering inspection.

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