Pelvic floor rehabilitation effect image contrast enhancement method

Abstract

The invention discloses a pelvic floor rehabilitation effect image contrast enhancement method, and belongs to the field of image processing. The objective of the invention is to improve the quality of pelvic floor X-ray images and the efficiency and accuracy of clinical diagnosis. According to the method, through collection compression and enhancement recovery modular design, whole-process image quality optimization is realized; the acquisition compression module retains low-contrast area information through dynamic resolution adjustment and area sensing compression, and the transmission bandwidth requirement is reduced; the enhancement and recovery module recovers the image resolution by using the mixed super-resolution and enhances the local and global contrast; in addition, the method further comprises a degradation modeling module, a loss function and training strategy module and a system integration module, and the enhancement effect of the generation network on the low-contrast image is optimized through multi-task learning training. According to the method, the defects of image degradation and noise processing are overcome, the balance between the image enhancement effect and diagnosis correlation is optimized by introducing a multi-task loss function, and powerful support is provided for clinical diagnosis and treatment.

Description

Technical Field

[0001] The present invention belongs to the field of image processing, and in particular relates to a method for enhancing the contrast of pelvic floor rehabilitation effect images. Background Art

[0002] Contrast enhancement of medical images plays an important role in disease diagnosis and treatment monitoring, especially in the field of pelvic floor rehabilitation. The evaluation of pelvic floor rehabilitation effects often relies on X-ray images. However, due to the low resolution, low contrast and noise artifacts of pelvic floor area images, it is difficult to accurately capture details during image interpretation. Traditional image processing methods such as histogram equalization and simple filtering can improve contrast to a certain extent, but often cannot simultaneously guarantee detail clarity and global image consistency. Therefore, it is particularly necessary to develop a specialized image contrast enhancement method based on the special imaging characteristics of pelvic floor rehabilitation effects. This method can not only improve image quality, but also enhance the efficiency and accuracy of clinical diagnosis and provide clearer imaging support.

[0003] Existing medical image contrast enhancement technologies have multiple limitations. First, most methods cannot simultaneously achieve both local and global contrast enhancement. In images of pelvic floor rehabilitation effects, low-contrast areas coexist with noise artifacts, making it difficult for simple global adjustment methods to meet the needs. Second, during the compression and transmission of images, image quality further deteriorates due to data loss and degradation, making it more difficult for doctors to identify key areas. In addition, most existing image enhancement algorithms focus on single-task optimization and fail to optimize image quality and diagnostic relevance in specific clinical application scenarios. Therefore, a comprehensive technical framework is needed that can significantly improve the contrast and diagnostic quality of pelvic floor rehabilitation images throughout the entire process of compression, transmission, and recovery.

[0004] In response to the above problems, the present invention proposes an innovative method for contrast enhancement of pelvic floor rehabilitation effect images, which combines the modular design of acquisition compression and enhancement restoration, and realizes full-process image quality optimization through dynamic resolution adjustment, region-aware compression and hybrid super-resolution restoration technologies; the acquisition compression module focuses on retaining information in low-contrast areas, and ensures the quality of transmitted images through strategies such as weighted complexity calculation and local attention weight allocation; the enhancement restoration module further restores and optimizes the global and local details of the image through nonlinear normalization, adaptive filtering and dynamic contrast enhancement; this method not only solves the shortcomings of traditional methods in image degradation and noise processing, but also optimizes the balance between image enhancement effect and diagnostic relevance of the generation network by introducing a multi-task loss function, significantly improving image quality and providing strong support for clinical diagnosis and treatment. Summary of the Invention

[0005] The present invention provides a method for contrast enhancement of pelvic floor rehabilitation effect images, aiming to significantly improve the contrast and diagnostic quality of pelvic floor rehabilitation images, thereby providing clearer image support for clinical diagnosis and treatment.

[0006] The present invention proposes a method for enhancing the contrast of pelvic floor rehabilitation effect images, which specifically includes the following steps: S1. Collect and create a pelvic floor X-ray dataset to generate training data pairs containing low resolution, low contrast, and noise artifacts; S2. Build an acquisition and compression PCM module to perform dynamic resolution adjustment and region-aware compression on the input medical images, prioritize low-contrast area information, and generate low-resolution compressed images; S3. Construct a degradation modeling (DGM) module to simulate the contrast degradation process during image transmission and build degradation models for low-contrast artifacts and uneven illumination. S4. Construct the loss function and training strategy LTS module, define the comprehensive loss function and perform multi-task learning training to optimize the enhancement effect of the generated network on low-contrast images; S5. Construct an enhanced restoration DRM module, receive low-resolution compressed images, use a hybrid super-resolution generation network to restore image resolution, and enhance local and global contrast; S6. Build a module integration system that integrates acquisition and enhanced processing modules to complete the entire process of image compression, transmission, restoration, and contrast enhancement.

[0007] Preferably, in step S2, constructing the acquisition and compression PCM module specifically includes the following steps: Step S21: Calculate the weighted complexity of the input image , then calculate the scaling factor S to achieve dynamic resolution compression, so as to give priority to retaining the spatial information of low-contrast areas. Through dynamic resolution adjustment and region-aware compression, low-contrast area information is given priority, and low-resolution compressed images are generated. This function can significantly reduce the bandwidth requirements for data transmission, while ensuring the retention of key medical information and improving data processing efficiency. The specific calculation formula is: ; ; Where S represents the resolution scaling factor, and its value range is (0,1]; Represents the weighted complexity of the input image; is the gradient weight of pixel (x, y), which is used to highlight the information of the area with lower contrast; is the intensity value of the pixel (x, y) in the input image; H and W are the height and width of the input image respectively.

[0008] Step S22: weighted edge strength of the image , dividing the image into key areas and non-critical areas , and perform region-aware compression on both. According to the weighted edge strength of the image, key areas and non-key areas are distinguished, and different compression strategies are adopted for different areas. This method effectively reduces unnecessary redundant data while enhancing the transmission accuracy of key medical information. The specific calculation formula is: ; ; in, Indicates the compressed image; is a nonlinear compression operator; and A higher compression quality factor for critical areas and a lower compression quality factor for non-critical areas, respectively; and They represent the key areas with higher weighted edge strength and the non-key areas with lower weighted edge strength, respectively.

[0009] Step S23: Calculate local attention weight , the specific calculation formula is: ; in, is the local attention weight, used to highlight contrast and structural features; is the noise reduction intensity control parameter; is the edge weight adjustment factor; Represents the gradient strength of pixel p.

[0010] Step S24: Use the local attention weight to perform noise smoothing on the image to obtain the PCM module compression output. Use the local attention weight to highlight the features of the low-contrast area and perform noise smoothing on the image to ensure the image transmission quality and improve the visualization effect of contrast and structural features. The specific calculation formula is: ; in, Represents the value of the smoothed image at pixel (x, y); Represents the local neighborhood of pixel (x,y).

[0011] Preferably, in step S3, constructing the degradation modeling module DGM module specifically includes the following steps: Step S31: simulate the contrast degradation caused by ringing artifacts due to uneven X-ray exposure using the optimized formula. The specific calculation formula is: providing real scene training data for the model, further optimizing the robustness of the enhancement algorithm; the specific calculation formula is: ; ; in, represents the input image pixel; Indicates contrast degradation caused by uneven illumination; Indicates contrast degradation caused by ringing artifacts; is the frequency parameter; is the intensity of X-ray illumination changes.

[0012] Step S32: Add degradation factors to generate blur and noise using the optimized formula to obtain the final degraded image. By adding blur, downsampling, and noise interference, a degraded image containing multiple defects is generated, providing challenging training samples for the subsequent enhancement module to ensure the algorithm's adaptability in complex scenarios. The specific calculation formula is: ; in, is the original X-ray image; g is the Gaussian blur kernel; is the downsampling operation; For noise interference; Indicates contrast degradation caused by uneven illumination; Indicates contrast degradation caused by ringing artifacts.

[0013] Preferably, in step S4, constructing the loss function and training strategy LTS module specifically includes the following steps: Step S41: Define a comprehensive loss function to optimize the contrast enhancement and edge detail restoration of the generated network. By optimizing the generated network through the comprehensive loss function, the focus is on contrast enhancement, structure preservation, and edge detail restoration. This function significantly improves the overall performance of image enhancement. The specific calculation formula is: ; Where, L is the total comprehensive loss; is the contrast perception loss; To maintain losses for the structure; Enhance loss for edges; is the weight coefficient of each loss item in the overall loss; Step S42: Design a contrast perception loss function based on the application scenario , the specific calculation formula is: ; in, and The grayscale value of the generated image and the real image at the pixel position (x, y); and is the horizontal and vertical gradient of the real image at the pixel position (x, y); the numerator of the formula as a whole represents the absolute error in pixel values ​​between the generated image and the real image; the denominator of the formula as a whole represents the loss weight for adjusting the low-contrast area, and the cross-gradient term is used to strengthen the attention of the area with weak contrast.

[0014] Step S43: Design a structure-preserving loss function based on the application scenario , the specific calculation formula is: ; in, To prevent the denominator from being a small positive number of zero; the numerator of the formula as a whole represents the dot product of the generated image and the real image, which is used to measure the correlation between pixels; the denominator of the formula as a whole represents the normalization term, which calculates the norm product of the generated image and the real image to ensure that the correlation value is in the range of [0,1].

[0015] Step S44: Design edge enhancement loss function according to application scenario , the specific calculation formula is: ; in, and Represents the second-order gradient of the generated image and the real image in the horizontal direction; and Represents the second-order gradient of the generated image and the real image in the vertical direction; and Represents the second-order gradient difference between the generated image and the real image edge, which is used to optimize the edge clarity.

[0016] Step S45: During the training process, combine the segmentation task Optimize the diagnostic relevance of the generated network, the total loss function The specific calculation formula is: ; in, is the segmentation loss based on the Dice coefficient, is the weight coefficient of the segmentation loss, which is used to control the impact of the segmentation task on the overall optimization.

[0017] Preferably, in step S5, constructing the enhanced recovery DRM module specifically includes the following steps: Step S51: Receive the low-resolution compressed image captured and transmitted The pixel value range is adjusted by normalizing the image through an improved nonlinear normalization function. After receiving the low-resolution compressed image, the pixel value is adjusted to a reasonable range through nonlinear normalization, and the hybrid super-resolution generation network is used to restore the image resolution. The lost detail information can be reconstructed to make the image more refined and convenient for analysis. The specific calculation formula is: ; Among them, k is the stretching parameter that controls the nonlinear compression of the pixel value range.

[0018] Step S52: The normalized image is subjected to local detail enhancement through directionally weighted adaptive filtering. Details in different directions are enhanced to restore and highlight subtle edges and texture features. This improves the restoration of image details and provides doctors with clearer observation conditions. The specific calculation formula is: ; in, is the enhanced local detail image; M is the number of directional filters; is the direction weight; To enhance the strength coefficient; is the convolution operation; is the filter in the mth direction; tanh() represents the hyperbolic tangent function.

[0019] Step S53: Global contrast enhancement of the image is performed by dynamically adjusting the contrast of the nonlinear mapping function, which can optimize the brightness level and contrast effect of the image and make the diagnosis area more prominent. The specific calculation formula is: ; in, This is the image after global contrast enhancement; is the local brightness mean of the image; Enhanced strength control parameters; Adjust the index for contrast.

[0020] Preferably, in step S6, a module integration system is constructed, specifically, first performing dynamic compression of the PCM module image on the batch training images, then performing image degradation simulation of the DGM module, and then inputting them into the LTS module to adjust the comprehensive loss, then generating a network model through multi-task learning training and passing the generated network model into the DRM module, and finally in the application stage inputting the real image to be processed into the DRM module for image contrast enhancement to obtain the final image enhancement output; the integrated system realizes full-process automated processing from acquisition, compression, transmission to recovery and enhancement. The system reduces the need for manual intervention, improves the overall processing efficiency, and ensures that the image quality meets the diagnostic requirements.

[0021] Compared with the existing technology, the present invention realizes the optimization processing of the whole process from image acquisition to compression, transmission and recovery through modular design; the acquisition module pays special attention to the retention of information in low-contrast areas, and effectively reduces the data transmission bandwidth requirement through weighted complexity calculation and local attention weight allocation, while ensuring the retention of key medical information; the enhancement and restoration module uses a hybrid super-resolution generation network to restore image resolution and enhance local and global contrast, significantly improving image quality; in addition, the present invention introduces a multi-task loss function to optimize the generation network's balance between image enhancement effect and diagnostic relevance, providing more powerful support for clinical diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure shows the overall flow chart of a method for contrast enhancement of pelvic floor rehabilitation effect images.

[0023] Figure 2 This is a schematic diagram of the structure flow of the acquisition and compression PCM module.

[0024] Figure 3 Schematic diagram of the structural flow of the degradation modeling DGM module.

[0025] Figure 4 This is a schematic diagram of the enhanced recovery DRM module structure flow.

[0026] Figure 5 The figure is a flow chart of the overall structure of the integrated system.

[0027] Figure 6 Schematic diagram of image comparison before and after enhancement processing. DETAILED DESCRIPTION

[0028] 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 only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of 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.

[0029] Please see the attached Figure 1 -Attached Figure 6 , the present invention provides a method for enhancing the contrast of pelvic floor rehabilitation effect images.

[0030] As attached Figure 1 The present invention proposes a method for enhancing the contrast of pelvic floor rehabilitation effect images, which includes the following steps: S1. Collect and create a pelvic floor X-ray dataset to generate training data pairs containing low resolution, low contrast, and noise artifacts; S2. Build an acquisition and compression PCM module to perform dynamic resolution adjustment and region-aware compression on the input medical images, prioritize low-contrast area information, and generate low-resolution compressed images; S3. Construct a degradation modeling (DGM) module to simulate the contrast degradation process during image transmission and build degradation models for low-contrast artifacts and uneven illumination. S4. Construct the loss function and training strategy LTS module, define the comprehensive loss function and perform multi-task learning training to optimize the enhancement effect of the generated network on low-contrast images; S5. Construct an enhanced restoration DRM module, receive low-resolution compressed images, use a hybrid super-resolution generation network to restore image resolution, and enhance local and global contrast; S6. Build a module integration system that integrates acquisition and enhanced processing modules to complete the entire process of image compression, transmission, restoration, and contrast enhancement.

[0031] Further, as attached Figure 1 The pelvic floor X-ray dataset described in S1 was generated by using modern medical imaging equipment to acquire pelvic floor X-ray images and generate a training dataset containing low resolution, low contrast, and noise artifacts. The dataset format adopts the standard DICOM format, and the images are normalized through preprocessing to make the image data dimension 512×512 pixels.

[0032] Further, as attached Figure 1 The construction steps of the acquisition compression PCM module described in S2 are as follows: Figure 2 As shown in the figure, the gradient weight is set to 0.1, the scaling factor range is 0.5-1.0, and the interpolation method is the cv2.INTER_AREA method in the OpenCV library. The specific implementation of the module includes the following steps: Furthermore, step S21 calculates the weighted complexity of the input image , then calculate the scaling factor S to achieve dynamic resolution compression, so as to give priority to retaining the spatial information of low-contrast areas. Through dynamic resolution adjustment and region-aware compression, low-contrast area information is given priority, and low-resolution compressed images are generated. This function can significantly reduce the bandwidth requirements for data transmission, while ensuring the retention of key medical information and improving data processing efficiency. The specific calculation formula is: ; ; Where S represents the resolution scaling factor, and its value range is (0,1]; Represents the weighted complexity of the input image; is the gradient weight of pixel (x, y), which is used to highlight the information of the area with lower contrast; is the intensity value of the pixel (x, y) in the input image; H and W are the height and width of the input image respectively.

[0033] Furthermore, step S22 is based on the weighted edge strength of the image , dividing the image into key areas and non-critical areas , and differentially compress the two, distinguishing key areas from non-key areas based on the weighted edge strength of the image, and adopting different compression strategies for different areas; this method effectively reduces unnecessary redundant data while enhancing the transmission accuracy of key medical information; the specific calculation formula is: ; ; in, Indicates the compressed image; is a nonlinear compression operator; and A higher compression quality factor for critical areas and a lower compression quality factor for non-critical areas, respectively; and They represent the key areas with higher weighted edge strength and the non-key areas with lower weighted edge strength, respectively.

[0034] Furthermore, step S23 calculates the local attention weight , the specific calculation formula is: ; in, is the local attention weight, used to highlight contrast and structural features; is the noise reduction intensity control parameter; is the edge weight adjustment factor; Represents the gradient strength of pixel p.

[0035] Furthermore, step S24 performs noise smoothing on the image using the local attention weight to obtain the PCM module compression output. The local attention weight is used to highlight the features of the low-contrast area and perform noise smoothing on the image to ensure the image transmission quality and improve the visualization effect of contrast and structural features. The specific calculation formula is: ; in, Represents the value of the smoothed image at pixel (x, y); Represents the local neighborhood of pixel (x,y).

[0036] Further, as attached Figure 1The construction process of the degradation modeling module DGM described in S3 is as shown in the attached Figure 3 As shown in the figure, the Gaussian blur kernel is set to 5×5, the blur intensity σ is 1.5, and the noise level is 0.05. The specific implementation of the module includes the following steps: Furthermore, step S31 simulates the contrast degradation caused by ringing artifacts due to uneven X-ray exposure using the optimized formula, providing real scene training data for the model and further optimizing the robustness of the enhancement algorithm. The specific calculation formula is: ; ; in, represents the input image pixel; Indicates contrast degradation caused by uneven illumination; Indicates contrast degradation caused by ringing artifacts; is the frequency parameter; is the intensity of X-ray illumination changes.

[0037] Furthermore, step S32 uses the optimized formula to add degradation factors to generate blur and noise, obtaining a final degraded image. By adding blur, downsampling, and noise interference, a degraded image containing multiple defects is generated, providing challenging training samples for the subsequent enhancement module, ensuring the algorithm's adaptability in complex scenarios. The specific calculation formula is: ; in, is the original X-ray image; g is the Gaussian blur kernel; is the downsampling operation; For noise interference; Indicates contrast degradation caused by uneven illumination; Indicates contrast degradation caused by ringing artifacts.

[0038] Further, as attached Figure 1 The loss function and training strategy LTS module described in S4 is constructed, where the contrast loss weight is set to 0.5; the structure loss weight is set to 0.3; and the edge loss weight is set to 0.2. The specific implementation of the module includes the following steps: Furthermore, in step S41, a comprehensive loss function is defined to optimize the contrast enhancement and edge detail restoration of the generated network. By optimizing the generated network through the comprehensive loss function, the focus is placed on contrast enhancement, structure preservation, and edge detail restoration. This function significantly improves the overall performance of image enhancement. The specific calculation formula is: ; Where, L is the total comprehensive loss; is the contrast perception loss; To maintain losses for the structure; Enhance loss for edges; is the weight coefficient of each loss item in the overall loss.

[0039] Furthermore, step S42 specifically designs the contrast perception loss function according to the application scenario. , the specific calculation formula is: ; in, and The grayscale value of the generated image and the real image at the pixel position (x, y); and is the horizontal and vertical gradient of the real image at the pixel position (x, y); the numerator of the formula as a whole represents the absolute error in pixel values ​​between the generated image and the real image; the denominator of the formula as a whole represents the loss weight for adjusting the low-contrast area, and the cross-gradient term is used to strengthen the attention of the area with weak contrast.

[0040] Furthermore, step S43 designs a structure-keeping loss function according to the application scenario. , the specific calculation formula is: ; in, To prevent the denominator from being a small positive number of zero; the numerator of the formula as a whole represents the dot product of the generated image and the real image, which is used to measure the correlation between pixels; the denominator of the formula as a whole represents the normalization term, which calculates the norm product of the generated image and the real image to ensure that the correlation value is in the range of [0,1].

[0041] Furthermore, step S44 designs the edge enhancement loss function according to the application scenario. , the specific calculation formula is: ; in, and Represents the second-order gradient of the generated image and the real image in the horizontal direction; and Represents the second-order gradient of the generated image and the real image in the vertical direction; and Represents the second-order gradient difference between the generated image and the real image edge, which is used to optimize the edge clarity.

[0042] Furthermore, step S45 combines the segmentation task during the training process. Optimize the diagnostic relevance of the generated network, the total loss function The specific calculation formula is: ; in, is the segmentation loss based on the Dice coefficient, is the weight coefficient of the segmentation loss, which is used to control the impact of the segmentation task on the overall optimization.

[0043] Further, as attached Figure 1 The enhanced recovery DRM module described in S5 is constructed, and the specific structure of the DRM module is as shown in the attached Figure 4 As shown in the figure, the horizontal and vertical weights are both set to 0.25; the enhancement factor is 1.5; the filter dimension is 3×3; the specific implementation of the module includes the following steps: Furthermore, step S51 receives the low-resolution compressed image captured and transmitted. The improved nonlinear normalization function is used to adjust the pixel value range. After receiving the low-resolution compressed image, the pixel value is adjusted to a reasonable range through nonlinear normalization, and the hybrid super-resolution generation network is used to restore the image resolution. It can reconstruct the lost detail information, making the image more refined and convenient for doctors to analyze. The specific calculation formula is: ; Among them, k is the stretching parameter that controls the nonlinear compression of the pixel value range.

[0044] Furthermore, step S52 performs detail enhancement on the normalized image through directionally weighted adaptive filtering, performs local detail enhancement on details in different directions, restores and highlights subtle edge and texture features, improves the restoration of image details, and provides doctors with clearer observation conditions. The specific calculation formula is: ; in, is the enhanced local detail image; M is the number of directional filters; is the direction weight; To enhance the strength coefficient; is the convolution operation; is the filter in the mth direction; tanh() represents the hyperbolic tangent function.

[0045] Furthermore, step S53 performs global contrast enhancement on the image by dynamically adjusting the contrast of the nonlinear mapping function, which can optimize the brightness level and contrast effect of the image and make the diagnosis area more prominent. The specific calculation formula is: ; in, This is the image after global contrast enhancement; is the local brightness mean of the image; Enhanced strength control parameters; Adjust the index for contrast.

[0046] Further, as attached Figure 1 The building block integration system described in S6 is as shown in the attached Figure 5 As shown in the figure; the specific implementation process is to first perform dynamic compression of the batch training images in the PCM module, then perform image degradation simulation in the DGM module, and then input them into the LTS module to adjust the comprehensive loss. Subsequently, a network model is generated through multi-task learning training and the generated network model is passed to the DRM module. Finally, in the application stage, the real image to be processed is input into the DRM module for image contrast enhancement to obtain the final image enhancement output; the integrated system realizes the full process automation from acquisition, compression, transmission to recovery and enhancement. The overall design process of the system is shown in the attached figure. Figure 5 The system reduces the need for manual intervention, improves overall processing efficiency, and ensures that the image quality meets diagnostic requirements. The image comparison diagram before and after processing by the system is shown in the attached figure. Figure 6 As shown, part a is the original image before processing, and part b is the output image after processing and enhancement.

Claims

1. A method for enhancing contrast of pelvic floor rehabilitation effect images, characterized in that: The following steps are involved: S1, collect and produce pelvic floor X-ray data sets, and generate training data pairs containing low resolution, low contrast and noise artifacts; S2, constructing an acquisition compression PCM module, dynamically adjusting the resolution and performing region-aware compression on the input medical images, giving priority to retaining low-contrast region information, and generating low-resolution compressed images; S3, build a degradation modeling DGM module to simulate the contrast degradation process in image transmission and build a degradation model of low-contrast artifacts and uneven illumination; S4, construct the loss function and training strategy LTS module, define the comprehensive loss function and perform multi-task learning training to optimize the enhancement effect of the generated network on low-contrast images; S5, construct an enhanced restoration DRM module, receive low-resolution compressed images, use a hybrid super-resolution generation network to restore image resolution, and enhance local and global contrast; S6. Build a module integration system that integrates acquisition and enhanced processing modules to complete the full process of image compression, transmission, restoration and contrast enhancement.

2. A pelvic floor rehabilitation effect image contrast enhancement method according to claim 1, characterized in that: In S2, constructing the acquisition compression PCM module includes the following steps: S21, by calculating the weighted complexity of the input image , and then calculate the scaling factor S to achieve dynamic resolution compression, so as to give priority to retaining the spatial information of low-contrast areas. The specific calculation formula is: ； ； Where S represents the resolution scaling factor, and its value range is (0,1]; Represents the weighted complexity of the input image; is the gradient weight of pixel (x, y), which is used to highlight the information of areas with lower contrast; is the intensity value of the pixel (x, y) in the input image; H and W are the height and width of the input image respectively; S22, according to the weighted edge strength of the image , dividing the image into key areas and non-critical areas , and perform region-aware compression on both. The specific calculation formula is: ； ； in, Represents the compressed image; is a nonlinear compression operator; and A higher compression quality factor for critical areas and a lower compression quality factor for non-critical areas, respectively; and They represent the key areas with higher weighted edge strength and the non-key areas with lower weighted edge strength respectively; S23. Calculate local attention weights , the specific calculation formula is: ； in, is the local attention weight, which is used to highlight contrast and structural features; It is the noise reduction intensity control parameter; is the edge weight adjustment factor; represents the gradient strength of pixel p; S24, the noise of the image is smoothed by the local attention weight to obtain the PCM module compression output. The specific calculation formula is: ； in, Represents the value of the smoothed image at pixel (x, y); Represents the local neighborhood of pixel (x,y).

3. The method for enhancing contrast of pelvic floor rehabilitation effect images according to claim 1, characterized in that: In S3, constructing the degradation modeling DGM module includes the following steps: S31. The optimized formula is used to simulate the contrast degradation caused by ringing artifacts caused by uneven X-ray exposure. The specific calculation formula is: ； ； in, represents the input image pixel; Indicates contrast degradation caused by uneven illumination; Indicates contrast degradation caused by ringing artifacts; is the frequency parameter; is the intensity of X-ray illumination variation; S32, adding degradation factors to generate blur and noise through the optimized formula to obtain the final degraded image. The specific calculation formula is: ； in, is the original X-ray image; g is the Gaussian blur kernel; It is a downsampling operation; For noise interference; Indicates contrast degradation caused by uneven illumination; Indicates contrast degradation caused by ringing artifacts.

4. The method for enhancing contrast of pelvic floor rehabilitation effect images according to claim 1, characterized in that: In S4, constructing the loss function and training strategy LTS module includes the following steps: S41. Define a comprehensive loss function to optimize the contrast enhancement and edge detail restoration of the generated network. The specific calculation formula is: ； Where, L is the total comprehensive loss; is the contrast perception loss; To maintain losses for the structure; Enhance loss for edges; is the weight coefficient of each loss item in the overall loss; S42. Design contrast perception loss function according to application scenario , the specific calculation formula is: ； in, and The grayscale value of the generated image and the real image at the pixel position (x, y); and is the horizontal and vertical gradient of the real image at the pixel position (x, y); the numerator of the formula as a whole represents the absolute error in pixel values ​​between the generated image and the real image; the denominator of the formula as a whole represents the loss weight for adjusting the low-contrast area, and the cross-gradient term is used to strengthen the focus of the area with weak contrast; S43. Design structure-preserving loss function according to application scenario , the specific calculation formula is: ； in, To prevent a small positive number with a denominator of zero; the numerator of the formula as a whole represents the dot product of the generated image and the real image, which is used to measure the correlation between pixels; the denominator of the formula as a whole represents the normalization term, which calculates the norm product of the generated image and the real image to ensure that the correlation value is in the range of [0,1]; S44. Design edge enhancement loss function according to application scenario , the specific calculation formula is: ； in, and Represents the second-order gradient of the generated image and the real image in the horizontal direction; and Represents the second-order gradient of the generated image and the real image in the vertical direction; and Represents the second-order gradient difference between the generated image and the real image edge, which is used to optimize the edge clarity; S45. Combine segmentation tasks during training Optimize the diagnostic relevance of the generated network, the total loss function The specific calculation formula is: ； in, is the segmentation loss based on the Dice coefficient, is the weight coefficient of the segmentation loss, which is used to control the impact of the segmentation task on the overall optimization.

5. The method for enhancing contrast of pelvic floor rehabilitation effect images according to claim 1, characterized in that: In S5, constructing the enhanced recovery DRM module includes the following steps: S51, receiving the low-resolution compressed image collected and transmitted , and the pixel value range is adjusted by normalization processing through an improved nonlinear normalization function. The specific calculation formula is: ； Among them, k is the stretching parameter, which controls the nonlinear compression of the pixel value range; S52, performing local detail enhancement on the normalized image through directional weighted adaptive filtering, and the specific calculation formula is: ； in, is the enhanced local detail image; M is the number of directional filters; is the direction weight; To enhance the strength coefficient; is the convolution operation; is the filter in the mth direction; tanh() represents the hyperbolic tangent function; S53, performing global contrast enhancement on the image by dynamically adjusting the contrast of the nonlinear mapping function, the specific calculation formula is: ； in, This is the image after global contrast enhancement; is the local brightness mean of the image; Enhanced strength control parameters; Adjust the index for contrast.

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