481 results about "Image restoration" patented technology

The invention discloses a plateau mountain road disaster identification method and system based on multi-source remote sensing image restoration and super-resolution reconstruction. The method comprises the following steps: acquiring and preprocessing a multi-source remote sensing image of a plateau mountain region, and extracting landform measurement parameters based on a digital elevation model; a super-resolution reconstruction network fusing deformable convolution and Transform is constructed, and a low-resolution image is reconstructed by using constraint training of a composite loss function containing geomorphic measurement parameters; performing feature extraction and adaptive weighted fusion on the preprocessed image and the reconstructed high-resolution image; based on the fused image, utilizing a multi-task deep learning model to identify landslide, debris flow and roadbed subsidence disasters along the highway; and carrying out morphological optimization and boundary refinement under GIS constraint on an identification result, and outputting a disaster thematic map. According to the invention, the precision and reliability of road disaster identification in a complex terrain environment are effectively improved.

The invention discloses an end-to-end defogging network fusing Mama and multi-scale context enhancement, and belongs to the technical field of computer vision and image restoration. The existing defogging technology has the problems of complex global modeling calculation, poor local detail recovery and insufficient feature fusion self-adaption. The problem is solved through the design of four core structures: 1, a Mamba state space model is introduced, and global fog long-range dependence modeling is realized with linear complexity; 2, designing a multi-scale cavity convolution module and a local context enhancement unit, and covering multi-scale local features; 3, constructing an enhanced adaptive fusion module to realize dynamic adaptation of global and local features; and 4, gradually recovering details by adopting a multi-scale input-cross-scale fusion-multi-scale output framework. The network can efficiently process high-resolution fog-containing images, and is suitable for scenes needing real-time high-precision defogging, such as automatic driving, security and protection monitoring, remote sensing imaging and the like.

The invention discloses a super-lens image restoration method based on fuzzy prior and semantic segmentation. The method comprises the following steps: eliminating an image visual angle displacement error by adopting a feature point automatic registration algorithm; the method comprises the following steps: designing a parallel multi-scale convolution adapter on the basis of an encoder optimized by a pre-trained visual Transform model, and generating encoding features adaptive to the degradation characteristics of a super lens; splicing the degraded image and the semantic segmentation mask into a joint tensor along a channel dimension, and obtaining a prior feature based on a fuzzy prior extraction network; performing spatial feature enhancement on the semantic segmentation mask based on a semantic segmentation graph convolutional network to obtain graph features aligned with the coding features; splicing the coding features, the prior features and the image features along channel dimensions to generate fusion features, and finally outputting a preliminary recovery image; and optimizing the whole model by adopting a staged training strategy, and finally outputting a high-quality recovery graph. According to the method, the physical rationality and detail fidelity of the recovery quality are improved.

The invention relates to the technical field of night monitoring, and discloses a night dynamic target tracking and image restoration method, which comprises the following steps of: establishing a heat-motion correlation model of a target through a space alignment algorithm in combination with heat source contour and intensity information acquired by a thermal imaging sensor and motion parameters detected by a millimeter wave radar; outputting an initial position and a motion state; inputting a target initial state and radar point cloud data based on an LSTM network, predicting a future N-frame trajectory, and dynamically correcting a prediction result by using real-time radar data; inputting the position information of the occlusion area and the trajectory prediction value into a GAN network to generate a complemented image, fusing the complemented image with the original heat source image, and recovering the complete contour of the target; and superposing and rendering the complemented image and the trajectory prediction into an AR picture, and when the target distance triggers a preset threshold value, realizing multi-channel safety early warning through vibration feedback, AR highlight warning and voice prompt. According to the invention, high-precision tracking and safety early warning of the night dynamic target can be improved.

The invention provides a method and a system for restoring a bamboo-strip character image based on multi-granularity feature guidance, and innovatively designs a coarse-fine two-stage restoration network and end-to-end multi-task loss joint training aiming at the problems of structure-texture confusion, non-uniform degradation, low contrast ratio and the like of the bamboo-strip image. In the coarse repair stage, a font texture and structure double-reconstruction sub-network is used for separating semantics from a source; in the fine repairing stage, multi-scale dynamic range distribution diagram self-attention (Mdma) is provided, pixels are dynamically classified according to degradation intensity, and long-short range dependence joint modeling is achieved; an adaptive mask is designed to sense pixel shuffling downsampling (Ampd), sampling is guided by mask confidence, damage position information is kept, and artifacts are inhibited. Five mainstream methods are compared on a homemade 313 bamboo strip single word data set, the PSNR, the SSIM and the FID are optimal under 0-60% irregular masks, visual evaluation of real missing samples is natural in texture, the structure is complete, and the readability of the bamboo strip characters and the subsequent recognition accuracy are effectively improved.

The invention relates to a document image restoration method and system, and the method comprises the steps: obtaining a to-be-restored document image, carrying out the preliminary reconstruction through a pre-trained preliminary restoration network, and obtaining a preliminary restoration image; document structured information of the to-be-repaired document graph is extracted, and text features are obtained based on the document structured information; performing text content coding, position information coding and confidence coefficient coding on each text feature, and performing fusion through a feature fusion module to generate a text control feature; inputting the text control feature and the preliminary restoration image into a ControlNet together to generate a control signal; and inputting the control signal into a pre-trained potential diffusion model to de-noise the potential spatial features step by step, and decoding the de-noised potential spatial features by using a pre-trained potential decoder to generate a repaired document image. The method and the device have the effect of improving the document image restoration efficiency and the restoration effect.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a low-light remote sensing image restoration method and system based on double-frequency domain processing, and the method comprises the steps: constructing an image restoration network which comprises a coding module, an intermediate enhancement module and a decoding module which are connected in sequence, the coding module and the decoding module are in jump connection; wherein the coding module, the intermediate enhancement module and the decoding module are each internally provided with a double-frequency-domain attention module, and each double-frequency-domain attention module comprises a Fourier attention sub-module used for global frequency domain feature modeling and a wavelet attention sub-module used for multi-scale detail feature extraction; by introducing the Fourier transform frequency domain processing technology, the global frequency characteristic analysis capability is provided, and efficient global modeling is realized. Secondly, introducing a wavelet decomposition frequency domain processing technology, decomposing the image into sub-bands with different scales and frequencies, and effectively separating a clear image and a degenerated component;

The invention relates to a self-adaptive selection restoration method for defocus blurred image restoration, and belongs to the technical field of image restoration. The method comprises the following steps of: establishing a multi-scale branch which consists of three coding blocks and decoding blocks and is used for processing images with different resolutions; in each branch, shallow layer features of an input image with the corresponding resolution are extracted through a convolutional layer, and then image reconstruction is carried out from the shallow layer features by self-adaptive selection modules in corresponding coding blocks and decoding blocks; wherein each self-adaptive selection module comprises a self-adaptive double-branch fractional order module and a double-path fusion strategy based on gating reweighting, so that image reconstruction and feature fusion among different branches are carried out respectively, and finally a reconstructed image is output. Compared with the prior art, the method provided by the invention can realize a more efficient restoration effect on the compressed blurred image with lower model parameter quantity and calculation complexity.

The invention belongs to the technical field of digital image processing, and discloses an image restoration method, system and device based on multi-modal large model driving. The method comprises the following steps: receiving a to-be-recovered degraded image, inputting the degraded image and a preset multi-task text cue word into a multi-modal large model, analyzing and processing the degraded image, and generating an operation instruction and a visual description prompt of the degraded image; encoding the operation instruction and the visual description prompt to respectively obtain a corresponding task intention vector and a content guide vector; and fusing the task intention vector and the content guide vector through an image restoration model, reconstructing the degraded image, and outputting a restored image. According to the method, the image problem can be automatically and accurately diagnosed, rich guidance information is provided from the two dimensions of operation and content, and intelligent, automatic and high-fidelity image recovery is realized.

The invention belongs to the technical field of target detection, and particularly relates to a radar image restoration and small target detection method based on adaptive sparse modeling, and the method comprises the steps: collecting original radar signal data, obtaining an initial low-quality radar image through preprocessing, and constructing a signal feature template; carrying out adaptive step size division, sparse decomposition and dynamic threshold judgment to obtain a characteristic coefficient matrix; a double-norm dynamic weighting optimization model and a local noise sensing mechanism are utilized, and alternate iterative optimization is combined, so that a high-quality restored image is obtained; obtaining a signal point enhanced image through a three-scale Gaussian kernel collaborative detection and information entropy quantization weighted fusion strategy; outputting a signal point correlation structure chart and a structured feature matrix; and outputting a small target category probability through training and parallel reasoning by adopting a fusion network of an image path and a graph structure path. The method has the capabilities of small target high-precision detection, cross-equipment efficient adaptation and real-time processing in a complex scene.

The invention discloses an image restoration method and device based on a rotation verification code and a storage medium, and the method comprises the steps: obtaining a verification code image, and analyzing the verification code image to obtain an original rotation angle; splitting the verification code image into an internal rotation image and an external background image, and inputting the internal rotation image and the external background image into a visual Transform model; capturing a long-distance dependency relationship between the internal rotation image and the external background image by adopting a self-attention mechanism of the visual Transform model, and outputting a predicted rotation angle based on the long-distance dependency relationship; and restoring the verification code image through the predicted rotation angle and a preset annular weight loss function. Through the self-attention mechanism of the visual Transform model, the long-distance dependency relationship between the internal rotation image and the external background image is effectively captured, and the rotation angle prediction precision is improved, so that the reliability of verification code image restoration and the accuracy of safety evaluation are enhanced.

The invention relates to a paper archive digital processing method and device, equipment and a medium. The method comprises the following steps: extracting image quality characteristics of a paper archive image, identifying a flaw region based on the image quality characteristics, and classifying flaw types through a pre-trained neural network; according to a preset defect type-image restoration algorithm mapping rule, restoring the defect area by adopting a corresponding algorithm to obtain a finished image; extracting a character sequence from the trimmed image by using an optical character recognition algorithm, encoding the character sequence into a semantic vector, and determining a document theme label in a pre-established archive theme library through vector similarity matching; and a multi-dimensional index structure is generated by combining the original image, the trimmed image and the character sequence, so that efficient retrieval and management of archive contents are realized. According to the method, the accuracy of defect repair and the recognition rate of optical character recognition are improved, and the automation level of digital processing is enhanced.

The invention discloses a hierarchical image rain removal method based on enhanced rain stripe perception. According to the method, for the problem of image quality degradation in a rainy day environment, an enhanced rain stripe perception feature enhancement module E-RAFEM is designed, two core components of a learnable direction filter and rain stripe texture modeling are integrated, and the directivity and linear texture features of rain stripes are accurately modeled. An encoder-decoder backbone network based on Transform is adopted, and E-RAFEM modules are embedded in the first three encoding levels, so that a hierarchical rain stripe processing mechanism is formed. A progressive multi-scale fusion mechanism is introduced, multi-scale features are captured through convolution kernels with different expansion rates, and gradual integration is carried out in a progressive mode to avoid information loss. According to the enhanced rain stripe sensing network ERA-Net provided by the invention, high-quality recovery of images under various complex rainy day conditions is realized through accurate rain stripe feature modeling and hierarchical processing strategies.

The invention relates to the technical field of image processing, in particular to an online calibration method and system for hydrological flow data, and solves the technical problem of low flow calculation precision caused by rough window segmentation and illumination interference in the prior art. The method comprises the following steps: collecting a water surface video in a river water flow process, and setting a velocity measurement line area in the water surface video along a water flow direction; according to the gray level distribution of the velocity measurement line area, carrying out illumination state judgment, and according to a judgment result, carrying out image restoration on an illumination abnormal area in the water surface video to obtain a restored water surface video; constructing a space-time image for the repaired water surface video, performing corner detection, clustering corners to divide a plurality of windows, and constructing a window weight for each window; and weighting the texture angle of each window according to the window weight of each window to obtain a weighted texture angle, and calculating hydrological flow data according to the weighted texture angle.

The invention discloses a hyper-stable imaging method. The method comprises the following steps: establishing a standard model of an optical system; acquiring boundary conditions of a working environment of the optical system to obtain N typical working conditions of the optical system; under each typical working condition, state change data of internal optical elements are obtained through corresponding analysis; substituting the state change data corresponding to the N typical working conditions into the standard model to respectively obtain a first working condition optical system to an Nth working condition optical system; obtaining an optimized optical system by using the first working condition optical system to the Nth working condition optical system, and forming a stable degradation model of the optical system; a standard image set and a degraded image set are generated using the standard model and the stable degradation model. The standard image set and the degraded image set form an image pair, and an image restoration algorithm is formed by using a neural network model, so that the quality of the degraded image is close to that of the standard image, and stable imaging of the system under different working conditions is realized.

The invention belongs to the technical field of image restoration and computer vision, and particularly relates to a multi-mode face restoration and expression recognition system and method based on semantic guidance of a facial action unit. According to the system, multi-scale features are extracted through visual coding, and an AU activation probability is detected by using a graph neural network; the semantic conversion module converts the numerical probability into an interpretable biomechanical structured text; the multi-modal reasoning module fuses vision and text information, introduces the common sense reasoning ability of a multi-modal large language model, and improves the student network performance through knowledge distillation; and finally, the conditional generation module realizes image restoration by taking the semantic features as guidance. The facial action unit is used as a biomechanical medium, the multi-modal reasoning ability is converted into restoration constraint, the defects that in the prior art, restoration of physiology is distorted, recognition depends on image quality, and two tasks are isolated are overcome, collaborative enhancement of face restoration and expression recognition is achieved, and it is ensured that the restoration result is clear in vision and conforms to the physiological law of facial muscles.

The invention provides an integrated image restoration method and system, and belongs to the field of image processing. The method comprises the steps that an integrated image restoration model TGMIR based on text guidance is constructed, and the integrated image restoration model TGMIR is used for mapping a text prompt to a semantic space consistent with image features and achieving hierarchical semantic regulation and control on three levels of channel attention, space attention and cross-modal collaborative attention; and recovering the image by using the integrated image recovery model TGMIR. The invention aims to break through the core limitation of the existing integrated image restoration model under the multi-degradation condition, including the problems of insufficient degradation semantics, obvious cross-degradation interference, weak modal cooperation capability, insufficient feature fusion and the like.

The invention discloses a turbulence restoration method combining super-resolution and a multi-scale network, belongs to the field of image restoration, and is suitable for remote sensing image restoration under the influence of atmospheric turbulence. The method comprises the following steps: constructing a training set and a test set; a multi-scale attention module is introduced into the generator, and shallow image features and deep image features under different scales are fully extracted; performing super-resolution up-sampling on the deep features to recover the spatial resolution, and fusing the deep features with the shallow features of the corresponding scales to generate a restored image; a multi-scale discriminator is adopted to carry out quality evaluation on the generated image, the image passing the evaluation is directly output, and the image not passing the evaluation is fed back to a generator by the discriminator to carry out reconstruction optimization; and finally outputting a restored image and obtaining a trained network model. According to the method, the spatial resolution and the structure restoration quality of the image can be effectively improved under the influence of atmospheric turbulence, and the identifiability of the image and the robustness of a subsequent visual task are enhanced.

The invention relates to an ultra-high-definition image restoration method, device and equipment based on clustering center feature scanning. The method comprises the steps that a clustering center scanning unit and a space-channel feature modulator are constructed, the clustering center scanning unit constructs a plurality of center features through a feature aggregation stage sparse sampling input feature map, a cosine similarity matrix of pixels and the center features is calculated, similar pixels are screened through an activation function and aggregated to generate clustering core features, and the clustering core features are clustered through the space-channel feature modulator; the similarity matrix is utilized to establish association mapping of core and non-core features through a fractional diffusion stage, global modeling weight is transmitted, a space-channel feature modulator extracts global features and texture detail features through parallel attention branches, the two modules are fused based on an asymmetric encoder-decoder architecture, and the texture detail features are obtained. According to the method, an ultra-high-definition image restoration network is constructed, low-quality ultra-high-definition images are restored after training, high-quality images are output, and the problems of video memory bottleneck and detail blurring in the prior art can be effectively solved.

The invention relates to the technical field of image restoration, in particular to a diffusion model image restoration method based on regional mask and dynamic exit, and mainly solves the technical problems of high calculation overhead and iteration step number redundancy of an existing diffusion model image restoration method. According to the method, on the basis of an extension architecture of encoding and decoding, a UNet denoising network and a ControlNet conditional constraint module, on the basis of retaining the core functions of an existing encoder, an existing decoder and an existing Unet denoising network, a space region mask generation sub-module, a back diffusion dynamic exit module and the ControlNet conditional constraint module are creatively integrated; and a triple optimization architecture of'constrain structure protection, space redundancy reduction and time step number reduction 'is formed, so that the structure consistency is met and efficient acceleration is realized during image restoration.

The invention relates to the technical field of scanning electron microscope image processing, and discloses a scanning electron microscope image restoration method based on a wavelet frequency domain adjustment diffusion model, and the method comprises the steps: constructing a two-stage degradation pipeline comprising random light path disturbance and fixed circuit collection limitation, and generating a specific domain training data set; extracting multi-scale high-frequency energy characteristic modulation noise distribution by using a frequency domain prior encoder, and training a wavelet frequency domain adjustment diffusion model; and inputting a target low-quality degraded image into the pre-training model, initializing a potential noisy state, then executing reverse denoising iteration, calculating wavelet domain consistency gradient correction noise prediction, and updating the potential noisy state until a restored image is generated. According to the method, data distribution mismatch is relieved through physical degradation modeling, high-frequency detail perception is enhanced by means of a frequency domain adjustment mechanism, the structural fidelity is improved through wavelet domain gradient guidance, and balance between scanning electron microscope image noise suppression and detail recovery is achieved.

The invention discloses a medical image restoration method based on text-driven prompting and double-domain modeling, and the method comprises the steps: S10, inputting an image ILQ, and extracting a shallow feature Fs through a convolution layer; s20, enabling the Fs to generate a depth feature Fd by using an N-level encoder decoder network with jump connection; on a decoder side, each level comprises NT dual-domain converters and an up-sampling convolution layer; each decoding hierarchy is guided by a text-driven prompt module enhanced by LLM, and the text-driven prompt module provides related task information and generates information prompt to guide space and frequency modeling in the two-domain converter; and S30, processing the depth feature Fd by using a convolutional layer, predicting a residual image Ir, and adding the residual image Ir to an input ILQ image to obtain a restored image. The method supports a plurality of medical image recognition tasks with different degradation types and severity, guides the generation of high-quality task related prompts, and supports the realization of more efficient and general image restoration in a plurality of degradation scenes.

The invention discloses an underwater polarization image restoration method based on Transform and depth estimation, and belongs to the technical field of underwater image processing. The method comprises the following steps: acquiring an underwater polarization image data set, and dividing the underwater polarization image data set into a training set and a test set; training an underwater polarization image restoration network based on Transform and depth estimation, the network comprising an encoder and a decoder, the encoder comprising a U-Net module, a multi-scale content guidance attention module, a depth estimation network and a multi-scale convergence attention module; the decoder comprises two feature fusion modules which are connected in series and guide attention based on multi-scale content; the trained underwater polarization image restoration network based on Transform and depth estimation is tested based on the test set; and carrying out image restoration by using the tested underwater polarization image restoration network based on Transform and depth estimation. According to the method, the restoration effect of the underwater image can be remarkably improved, the image texture information is enhanced, and the definition and detail performance of the image are improved.

The invention relates to the technical field of computer image restoration, in particular to a frequency domain and spatial domain adaptive collaborative modeling image defogging method, which comprises the following steps: acquiring a foggy image, and inputting the foggy image into a trained image defogging model to obtain a defogged image; the training process of the image defogging model comprises the following steps: inputting a foggy image into the shallow feature extraction module to obtain a preliminary feature; inputting the preliminary features into a frequency domain enhanced attention encoder to obtain advanced features; inputting the advanced features into a frequency domain enhanced attention decoder to obtain residual features; adding the foggy image and the residual features of the foggy image to obtain a defogged image; calculating a loss function value according to the fogless image and the defogged image of the foggy image, updating parameters of the model according to the loss function value, and obtaining a trained model when the loss function value is minimum; according to the method, a frequency domain attention enhancement module is introduced in the encoding and decoding processes, a defogging model for frequency domain and space domain adaptive collaborative modeling is constructed, and the defogging stability and definition are improved.

The invention discloses an end-to-end image recovery method and system based on image-text feature mapping and a medium, and relates to the technical field of image processing.The method comprises the steps that a sample set comprising degraded and clean images is called, and a four-layer feature space is established through an image-text feature mapping mechanism; analyzing the degradation features by adopting an isometric tight frame, constructing a prompt vector set, and guiding a feature space to carry out adaptive dynamic prompt fusion to form a dynamic prompt vector set; a first model is established through image recovery loss function iterative training, then an AdamW optimizer is used for gradient descent optimization to obtain a second model, and finally image recovery processing is executed. The technical problem that an existing image restoration method is unstable in restoration effect and poor in adaptability in the multi-degradation scene is solved, and the technical effect that the image restoration precision and adaptability in the multi-degradation scene are improved by introducing a dynamic prompt generation and hierarchical fusion mechanism is achieved.

The invention discloses a dynamic shelter restoration method and system based on continuous streetscape panoramic images, and the method comprises the steps: firstly obtaining a to-be-restored target panoramic image A and a to-be-restored reference panoramic image B, and generating an original pixel-level shelter mask; secondly, extracting matching points among the panoramic images, realizing cross-view geometric alignment of the images, and obtaining a target perspective view C and a reference perspective view D through perspective re-projection; and then inputting the target perspective view C and the reference perspective view D into a three-dimensional reconstruction framework, and performing three-dimensional point cloud reimaging under the camera pose of the target perspective view C by using a depth inspection mechanism. And finally, carrying out image restoration on a three-dimensional point cloud re-imaging result, restoring to a panoramic coordinate system, splicing with an original panoramic image, and outputting a shielding-free panoramic image. According to the method, it is ensured that the repairing result conforms to the authenticity and geometric consistency of the geographic space, and the problems of overlapping conflicts and visual tearing during multi-view projection fusion are effectively solved.

The invention provides an image compression method and device based on cooperation of frequency domain transformation and high-frequency denoising, and the method comprises the steps: a data preparation step: carrying out the cutting and preprocessing of a target optical image, and generating a composite image containing various types and intensities of high-frequency noise for training and testing; a combined image coding and denoising step: setting a double-branch image coding and denoising module which comprises a main branch and a side branch sharing parameters; the main branch takes a noisy image as input, the side branch takes a corresponding clean image as input, the main branch is guided to synchronously learn and denoise in an image feature coding process, and a noiseless image coding feature is output; a frequency domain transformation denoising unit is embedded in the double-branch image coding denoising module; and a feature compression and image restoration step: carrying out quantization and entropy coding on the noiseless image coding features to generate a compressed code stream, and obtaining a final denoised restored image through decoding and image reconstruction.

The invention is suitable for the technical field of image restoration, and provides a space controllable generation guided unified image restoration method and model, and the method comprises the following steps: S1, image extraction: employing a pre-training model to extract a depth map and text description from a degraded image, the depth map being extracted through a pre-training depth map extraction model Depth-Anything-V2, and the text description being extracted through a pre-training depth map extraction model Depth-Anything-V2; the text description is extracted through a pre-training text description model CogVLM2, and the depth map is used for controlling the overall space of the reference map to be consistent with the degradation map. According to the unified image restoration method and model guided by spatial controllable generation, the spatial controllable reference generation model guided by a multi-granularity condition is introduced by improving the quality of a reference image and improving the restoration controllability, and the model can generate an intermediate reference image which is consistent in spatial structure and does not contain degradation on the basis of multi-granularity condition input; information support is provided for final restoration, degradation information can be effectively filtered out, and high-quality generation of the reference image is achieved.

A device comprising an image processor configured to implement: a first machine learning model for performing restoration processing on degraded image data; and a second machine learning model for recognizing areas of an image requiring processing emphasis during the restoration processing, wherein the output of the second machine learning model is an input to the first machine learning model to optimize the restoration processing.

The invention provides an image restoration method and system based on reverse cascade energy structure co-evolution, a medium and equipment, and belongs to the technical field of image processing and image restoration. The system comprises: a front-end CNN configured to perform visual feature extraction to obtain an initial feature map; the distance map generator is used for calculating the shortest Euclidean distance from each pixel in the damaged area to the known area to obtain a distance map; the damaged area is divided into equidistant concentric annular belts; the boundary statistical encoder is used for calculating a statistical moment of each annular belt and a joint feature of each pixel in each annular belt; the reverse band cascade HDNN is used for calculating the Hamiltonian amount of each annular band by using a Hamiltonian function and executing half-step symplectic evolution according to a sequence from an outer band to an inner band, and the Transform repair network is used for repairing a damaged area by using the Transform repair network by taking an evolution result as input and outputting a repaired image. According to the scheme, the structural consistency of damaged image restoration can be ensured, distortion is avoided, and the restoration effect is improved.