Image enhancement method and system based on adaptive filtering and polarization weighted fusion

Abstract

The invention relates to an image enhancement method and system based on adaptive filtering and polarization weighted fusion. The method comprises the following steps: S10, obtaining an original polarization image of a polarization camera; s20, according to the original polarization image, performing stokes vector calculation to obtain a maximum light intensity image, a minimum light intensity image and a linear polarization degree image; s30, performing low-pass filtering processing on the maximum light intensity image and the minimum light intensity image through an adaptive threshold low-pass filtering algorithm to obtain a filtered image; and S40, carrying out weighted fusion on the filtered image and the linear polarization degree image to obtain an image with clear texture. Through the adaptive threshold low-pass filtering algorithm, a clear underwater scene can be recovered under the condition of no background prior information, and the threshold can be dynamically set according to the image content, so that the noise and unnecessary details are reduced while the important structure and texture information of the image are reserved.

Description

Technical Field

[0001] The present disclosure relates to the field of computer vision, and in particular to an image enhancement method and system based on adaptive filtering and polarization weighted fusion. Background Art

[0002] In recent years, underwater optical imaging technology has been widely used in the fields of marine resource exploration, seabed topography mapping, and underwater ecological environment monitoring. However, due to the absorption and scattering of light when it propagates in water, the quality of underwater imaging often decreases significantly. These factors work together to cause a sharp drop in the contrast of underwater images and blur the details of the target. Therefore, how to effectively suppress scattering interference and extract key information of underwater targets in complex turbid water environments has become an important research topic. The underwater image enhancement method based on adaptive threshold filtering and weighted fusion can automatically adjust the filter parameters according to the local changes of the image through adaptive threshold filtering, so as to accurately remove noise and retain the detailed information of the image. The weighted fusion technology can better balance the relationship between enhancement effect and image quality by combining the advantages of multiple enhancement methods, avoid over-enhancement or over-denoising, and obtain the optimal underwater image.

[0003] Traditional underwater image enhancement algorithms mainly rely on a variety of image processing techniques to improve image quality by optimizing and enhancing the features of underwater images. These methods generally include: image enhancement based on histogram equalization, which enhances the contrast of the image by adjusting the grayscale distribution of the image; image enhancement based on Retinex theory, which improves the color and contrast of the image by estimating the illumination and reflection components; and denoising methods based on filtering algorithms, which remove noise by smoothing the image while retaining the edges and details of the image. However, these traditional methods often find it difficult to effectively deal with problems such as color distortion, blur and low contrast of underwater images when facing complex underwater environments, resulting in poor enhancement effects and limitations in the restoration of image details. Therefore, an underwater image enhancement method based on adaptive threshold filtering is needed to automatically set an adaptive threshold according to different areas of the image, so that the filtering process is more flexible and precise. It is also necessary to enhance the local details of the image while improving the clarity, contrast and information retention of the underwater image, so that the final enhanced image is more natural and detailed in visual effect. Summary of the invention

[0004] In order to solve the problems of repeated exploration, incomplete environmental coverage, and easy falling into local optimum when facing dynamic changes, complex obstacles, and environments that cannot be known in advance, the traditional robot exploration method proposes an image enhancement method based on adaptive filtering and polarization weighted fusion to solve the above problems.

[0005] According to one aspect of the present disclosure, an image enhancement method based on adaptive filtering and polarization weighted fusion is provided, comprising: S10, obtaining an original polarization image from a polarization camera; S20, according to the original polarization image, using Stokes vector calculation to obtain maximum and minimum light intensity images and linear polarization degree images; S30, performing low-pass filtering on the maximum and minimum light intensity images by using an adaptive threshold low-pass filtering algorithm to obtain a filtered image; S40, performing weighted fusion on the filtered image and the linear polarization degree image to obtain an image with clear texture.

[0006] Preferably, the maximum and minimum light intensity images and the linear polarization degree image are obtained by using Stokes vector calculation, which are expressed as: , , in, , In the formula, represents the position of the pixel, is the linear polarization image, , , and Respectively represent the light intensity after polarization filtering at 0°, 90°, 45° and 135°. is the angle between the horizontal direction and the transmission axis of the polarizer. Angle, calculate the maximum and minimum light intensity images.

[0007] Preferably, the maximum and minimum light intensity images are subjected to low-pass filtering by an adaptive threshold low-pass filtering algorithm, including: optimizing the low-pass filtering according to the adaptive threshold, when When , the intensity of the backscattered light is maintained after low-pass filtering; when When , the intensity of the original light intensity image remains unchanged, which can be expressed as: , In the formula, represents an image containing only the backscattered light intensity distribution, is the adaptive threshold, To optimize the backscattered light intensity, represents the position of the pixel, is the original image.

[0008] Preferably, optimizing the low-pass filtering according to the adaptive threshold comprises: calculating the adaptive threshold using the normalized gradient, wherein the adaptive threshold is calculated as follows: , In the formula, is the normalized gradient, is the size of the image, Represents the input image.

[0009] Preferably, optimizing the low-pass filtering according to the adaptive threshold value further includes: optimizing the low-contrast area by using the adaptive threshold low-pass filtering algorithm, and normalizing the gradient values ​​of different image areas, which is expressed as: , in, , , , In the formula, is the total gradient, Represents the maximum value of all gradient values ​​in the image. represents the input image and * represents the convolution operation.

[0010] Preferably, weighted fusion of the filtered image and the linear polarization image includes: calculating the optimal weights of the filtered image and the linear polarization image based on the structural similarity index, the visual information fidelity and the contrast weight, expressed as: , , , , , In the formula, represents the image after filtering. represents the polarization image, represents the structural similarity index, represents the fidelity of visual information, is the contrast weight of the image, represents the adjustment factor, , Respectively represent images x and images y The mean of , Respectively represent images x and images y The variance of Representing images x and images y The covariance of and is the stability constant, Representing images x and images y The mutual information between Representing images x Self-information, Indicates the image i The brightness value of a pixel, Represents the average brightness value of the image, N Indicates the total number of pixels.

[0011] Preferably, weighted fusion of the filtered image and the linear polarization image includes: weighted fusion of the optimal weights of the filtered image and the linear polarization image, expressed as: , In the formula, Represents the weight of the image after filtering. represents the image after filtering. represents the weight of the polarization image, Represented as a polarization image.

[0012] According to one aspect of the present disclosure, an image enhancement system based on adaptive filtering and polarization weighted fusion is provided, comprising: An original polarization image acquisition module is used to acquire the original polarization image of the polarization camera; The light intensity image and linear polarization image acquisition module obtains the maximum and minimum light intensity images and linear polarization image by using Stokes vector calculation according to the original polarization image; A low-pass filtering processing module performs low-pass filtering on the maximum and minimum light intensity images through an adaptive threshold low-pass filtering algorithm to obtain a filtered image; The image weighted fusion module performs weighted fusion on the filtered image and the linear polarization image to obtain an image with clear texture.

[0013] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to: execute the above-mentioned image enhancement method based on adaptive filtering and polarization weighted fusion.

[0014] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above-mentioned image enhancement method based on adaptive filtering and polarization weighted fusion is implemented.

[0015] Compared with the prior art, the beneficial effects of the present disclosure are: 1) The method disclosed in the present invention uses the Stokes vector to generate the optimal image pair, which can overcome the limitation of the traditional method that relies on manual rotation of the polarizer and reduce the image acquisition time.

[0016] 2) The present invention uses an adaptive threshold low-pass filtering algorithm to restore clear underwater scenes without background prior information and can reduce noise and unnecessary details while retaining important image structure and texture information by dynamically setting the threshold according to the image content.

[0017] 3) The present invention can significantly improve the detail clarity and global contrast of the fused underwater image.

[0018] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

[0019] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0021] Figure 1 A flow chart of an image enhancement method based on adaptive filtering and polarization weighted fusion is shown; Figure 2 The structural block diagram of polarization weighted fusion in the example of the present disclosure is shown; Figure 3 The structural block diagram of the image enhancement system based on adaptive filtering and polarization weighted fusion in the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0022] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0023] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0024] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0025] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0027] Example 1 Based on the above ideas, the present invention proposes an image enhancement method based on adaptive filtering and polarization weighted fusion. Figure 1 A flow chart of an image enhancement method based on adaptive filtering and polarization weighted fusion is shown. The method comprises: S10, obtaining an original polarization image from a polarization camera; S20, according to the original polarization image, using Stokes vector calculation to obtain maximum and minimum light intensity images and linear polarization degree images; S30, performing low-pass filtering on the maximum and minimum light intensity images by using an adaptive threshold low-pass filtering algorithm to obtain a filtered image; S40, performing weighted fusion on the filtered image and the linear polarization degree image to obtain an image with clear texture.

[0028] The present disclosure provides an image enhancement method based on adaptive filtering and polarization weighted fusion, which specifically includes the following steps: S10. Acquire an original polarization image from a polarization camera.

[0029] In this embodiment, the original polarization image is obtained by an array polarization camera, which adopts the principle of focal plane segmentation polarization imaging. The original polarization image contains four images with different polarization angles, namely 0°, 45°, 90° and 135°. These polarization images are taken by using a camera equipped with a focal plane segmentation array sensor (such as the IMX250MYR model). The principle of the polarization sensor in the polarization camera is that a layer of polarizer is covered on the photodiode of the traditional CMOS image sensor, and four polarizers with different angles (90°, 45°, 135° and 0°) are placed on a single pixel in turn, and every four pixels form a calculation unit. By analyzing the relationship between these polarizers, the degree and direction of polarization can be calculated. By directly processing the image obtained by the polarization camera, the tedious steps of segmentation, reorganization and recalculation are avoided, thereby simplifying the complexity of the system and improving the overall processing speed.

[0030] S20. According to the original polarization image, the maximum and minimum light intensity images and the linear polarization degree image are obtained by using Stokes vector calculation.

[0031] In this embodiment, the maximum and minimum light intensity images and the linear polarization degree image are obtained by using Stokes vector calculation, which are expressed as: , , in, , In the formula, represents the position of the pixel, is the linear polarization image, , , and Respectively represent the light intensity after polarization filtering at 0°, 90°, 45° and 135°. It is the angle between the horizontal direction and the transmission axis of the polarizer. By setting a specific angle, the maximum and minimum light intensity images are calculated.

[0032] S30, performing low-pass filtering on the maximum and minimum light intensity images using an adaptive threshold low-pass filtering algorithm to obtain a filtered image.

[0033] In this embodiment, considering that the backscattered light mainly affects the low-frequency part of the image, the targetless background area is not selected for calculation, but the entire image is low-pass filtered in the frequency domain to accurately estimate the entire intensity B. The process is to first transform the intensity image into the frequency domain through the Fourier function, then multiply it with the transfer function of the filter, and the result of the multiplication is returned to the spatial domain image through the inverse Fourier transform.

[0034] , In the formula, and are the abbreviations for Fourier transform and inverse Fourier transform, respectively. represents an image containing only the backscattered light intensity distribution, is a low-pass filter, Represents the coordinates in the frequency domain.

[0035] However, this method has a certain impact on the low-contrast areas of the target, that is, the backscattered light intensity obtained by low-pass filtering in these low-contrast areas will have a certain error. In order to further improve the estimation accuracy of the polarization degree of backscattered light, this example uses an adaptive threshold low-pass filtering algorithm to optimize the low-contrast area, which can effectively reduce the errors that may occur in the low-pass filtering process.

[0036] The maximum and minimum light intensity images are subjected to low-pass filtering by using an adaptive threshold low-pass filtering algorithm, including: optimizing the low-pass filtering according to the adaptive threshold, when When , the intensity of the backscattered light is maintained after low-pass filtering; when When , the intensity of the original light intensity image remains unchanged, which can be expressed as: , In the formula, represents an image containing only the backscattered light intensity distribution, is the adaptive threshold, To optimize the backscattered light intensity, represents the position of the pixel, is the original image.

[0037] Optimizing the low-pass filtering according to the adaptive threshold includes: calculating the adaptive threshold using the normalized gradient, and the calculation formula of the adaptive threshold is: , In the formula, is the normalized gradient, is the size of the image, Represents the input image.

[0038] Furthermore, optimizing the low-pass filtering according to the adaptive threshold value also includes: optimizing the low-contrast area by using the adaptive threshold low-pass filtering algorithm, and normalizing the gradient values ​​of different image areas, which is expressed as: , in, , , , In the formula, is the total gradient, Represents the maximum value of all gradient values ​​in the image. represents the input image and * represents the convolution operation.

[0039] S40, performing weighted fusion on the filtered image and the linear polarization degree image to obtain an image with clear texture.

[0040] In this embodiment, the structural block diagram of polarization weighted fusion is as follows: Figure 2 As shown, Figure 2 The working principle and process of the image fusion module in the underwater image enhancement method based on adaptive threshold filtering and polarization weighted fusion are demonstrated. Figure 2 The following steps are described: Input image: First, input two images, which are images processed by adaptive low-pass filtering (Target Image) And the degree of linear polarization image (DoLP Image) .

[0041] Image Quality Evaluation Unit (IQEU): The two input images are evaluated by the image quality evaluation unit. This unit consists of three parts: structural similarity index (SSIM), which is used to evaluate the structural similarity of two images; visual information fidelity (VIF), which is used to evaluate the visual information fidelity of the image; contrast weight (CW), which is used to evaluate the contrast of the image.

[0042] Weight calculation: According to the results of the image quality assessment unit, the weights of the two images are calculated.

[0043] Weighted fusion: Apply the calculated weights to the input images for weighted fusion to generate the final fused image.

[0044] Output image: The final output fused image has obvious texture details and clear image quality.

[0045] Through this polarization weighted fusion method, the advantages of the image processed by adaptive low-pass filtering and the polarization image can be fully utilized to generate high-quality underwater images. The image processed by adaptive low-pass filtering and the polarization image are weightedly fused to fully utilize the advantages of both to achieve better image reconstruction.

[0046] In the weighted fusion process, this example uses an image quality evaluation unit (IQEU) composed of a structural similarity index (SSIM), visual information fidelity (VIF), and contrast weight (CW) to reasonably retain the superior information of different source images and calculate the optimal weights of the filtered image and the linear polarization image, which is expressed as: , , , , , In the formula, represents the image after filtering. represents the polarization image, represents the structural similarity index, represents the fidelity of visual information, is the contrast weight of the image, represents the adjustment factor, , Respectively represent images x and images y The mean of , Respectively represent images x and images y The variance of Representing images x and images y The covariance of and is the stability constant, Representing images x and images y The mutual information between Representing images x Self-information, Indicates the image i The brightness value of a pixel, Represents the average brightness value of the image, N Represents the total number of pixels. The optimal weight map is obtained by optimizing the weights of the image processed by adaptive low-pass filtering and the polarization image, and the final fused image is obtained by fusion using the optimal weight map.

[0047] Furthermore, weighted fusion of the filtered image and the linear polarization image includes: weighted fusion of the optimal weights of the filtered image and the linear polarization image, which is expressed as: , In the formula, Represents the weight of the image after filtering. represents the image after filtering. represents the weight of the polarization image, Represents the polarization degree image.

[0048] The disclosed embodiment uses an adaptive threshold low-pass filtering algorithm to perform low-pass filtering on the light intensity image to achieve accurate suppression of backscattered light, and can effectively restore the clarity of underwater scene images without prior background knowledge. The disclosed example also uses a weighted fusion algorithm to weighted fuse the image processed by the adaptive low-pass filtering with the polarization image, thereby generating a high-quality underwater image with high clarity and obvious texture details.

[0049] Example 2 As another aspect of the embodiment of the present disclosure, an image enhancement system 100 based on adaptive filtering and polarization weighted fusion is also provided. Figure 3 As shown, including: The original polarization image acquisition module 1 acquires the original polarization image of the polarization camera; The light intensity image and linear polarization image acquisition module 2 obtains the maximum and minimum light intensity images and the linear polarization image by using the Stokes vector calculation according to the original polarization image; A low-pass filtering processing module 3 performs low-pass filtering on the maximum and minimum light intensity images through an adaptive threshold low-pass filtering algorithm to obtain a filtered image; The image weighted fusion module 4 performs weighted fusion on the filtered image and the linear polarization image to obtain an image with clear texture.

[0050] In the absence of any contradiction, the above modules in the system of the embodiment of the present disclosure can implement any implementation of the above method.

[0051] Based on the description of the above embodiments, it can be seen that the embodiments of the present disclosure can achieve the following technical effects: 1) The method disclosed in the present invention uses the Stokes vector to generate the optimal image pair, which can overcome the limitation of the traditional method that relies on manual rotation of the polarizer and reduce the image acquisition time.

[0052] 2) The present invention uses an adaptive threshold low-pass filtering algorithm to restore clear underwater scenes without background prior information and can reduce noise and unnecessary details while retaining important image structure and texture information by dynamically setting the threshold according to the image content.

[0053] 3) The present invention can significantly improve the detail clarity and global contrast of the fused underwater image.

[0054] The embodiment of the present disclosure also proposes an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above-mentioned image enhancement method based on adaptive filtering and polarization weighted fusion. The electronic device can be provided as a terminal, a server, or other forms of equipment.

[0055] The present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the image enhancement method based on adaptive filtering and polarization weighted fusion is implemented. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0056] Those skilled in the art will understand that, in the above-mentioned image enhancement method and system based on adaptive filtering and polarization weighted fusion in the specific implementation mode, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process, and the specific execution order of each step should be determined by its function and possible internal logic.

[0057] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0058] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. An image enhancement method based on adaptive filtering and polarization weighted fusion, characterized in that: The steps include: S10, obtaining an original polarization image from a polarization camera; S20, according to the original polarization image, using Stokes vector calculation to obtain maximum and minimum light intensity images and linear polarization degree images; S30, performing low-pass filtering on the maximum and minimum light intensity images by using an adaptive threshold low-pass filtering algorithm to obtain a filtered image; S40, performing weighted fusion on the filtered image and the linear polarization degree image to obtain an image with clear texture.

2. The method according to claim 1, characterized in that The maximum and minimum light intensity images and linear polarization degree images are obtained by Stokes vector calculation, which are expressed as: ， ， in, ， In the formula, represents the position of the pixel, is the linear polarization image, , , and Respectively represent the light intensity after polarization filtering at 0°, 90°, 45° and 135°. is the angle between the horizontal direction and the transmission axis of the polarizer. Angle, calculate the maximum and minimum light intensity images.

3. The method according to claim 1, characterized in that The maximum and minimum light intensity images are subjected to low-pass filtering processing by an adaptive threshold low-pass filtering algorithm, including: optimizing the low-pass filtering according to the adaptive threshold, when When , the intensity of the backscattered light is maintained after low-pass filtering; when When , the intensity of the original light intensity image remains unchanged, which can be expressed as: ， In the formula, represents an image containing only the backscattered light intensity distribution, is the adaptive threshold, To optimize the backscattered light intensity, represents the position of the pixel, is the original image.

4. The method according to claim 3, characterized in that Optimizing the low-pass filtering according to the adaptive threshold includes: calculating the adaptive threshold using the normalized gradient, and the calculation formula of the adaptive threshold is: ， In the formula, is the normalized gradient, is the size of the image, Represents the input image.

5. The method according to claim 4, characterized in that Optimizing the low-pass filtering according to the adaptive threshold also includes: optimizing the low-contrast area using the adaptive threshold low-pass filtering algorithm, and normalizing the gradient values ​​of different image areas, which is expressed as: ， in, ， ， ， In the formula, is the total gradient, Represents the maximum value of all gradient values ​​in the image. represents the input image and * represents the convolution operation.

6. The method according to claim 1, characterized in that The weighted fusion of the filtered image and the linear polarization image includes: calculating the optimal weights of the filtered image and the linear polarization image based on the structural similarity index, the visual information fidelity and the contrast weight, which is expressed as: ， ， ， ， ， In the formula, represents the image after filtering, represents the polarization image, represents the structural similarity index, represents the fidelity of visual information, is the contrast weight of the image, represents the adjustment factor, , Respectively represent images x and images y The mean of , Respectively represent images x and images y The variance of Representing images x and images y The covariance of and is the stability constant, Representing images x and images y The mutual information between Representing images x Self-information, Indicates the image i The brightness value of a pixel, Represents the average brightness value of the image, N Indicates the total number of pixels.

7. The method according to claim 6, characterized in that The weighted fusion of the filtered image and the linear polarization image includes: weighted fusion of the optimal weights of the filtered image and the linear polarization image, which is expressed as: ， In the formula, Represents the weight of the image after filtering. represents the image after filtering, represents the weight of the polarization image, Represents the polarization degree image.

8. Image enhancement system based on adaptive filtering and polarization weighted fusion, characterized in that: include: An original polarization image acquisition module is used to acquire the original polarization image of the polarization camera; The light intensity image and linear polarization image acquisition module obtains the maximum and minimum light intensity images and linear polarization image by using Stokes vector calculation according to the original polarization image; A low-pass filtering processing module performs low-pass filtering on the maximum and minimum light intensity images through an adaptive threshold low-pass filtering algorithm to obtain a filtered image; The image weighted fusion module performs weighted fusion on the filtered image and the linear polarization image to obtain an image with clear texture.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the image enhancement method based on adaptive filtering and polarization weighted fusion according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the image enhancement method based on adaptive filtering and polarization weighted fusion as described in any one of claims 1 to 7 is implemented.

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