1639 results about "Image restoration" patented technology

The present application discloses a face image restoration method based on a state space model, which includes inputting the to-be-restored face image to a restoration model to obtain a restored face image; the restoration model includes an encoder and a decoder; the encoder sequentially includes a first image fusion module, a first multi-scale state space module, a second image fusion module, a second multi-scale state space module, a third image fusion module, and a third multi-scale state space module; the decoder sequentially includes a fourth multi-scale state space module, a first multi-scale attention fusion module, a fifth multi-scale state space module, a second multi-scale attention fusion module, a sixth multi-scale state space module, and a third multi-scale attention fusion module. This method ensures the consistency of face semantic information while restoring the detailed texture, especially low quality face images in real degraded scenes can achieve better restoration results.

The invention provides a self-adaptive underwater image enhancement method and system based on a Retinex theory and Mama, and relates to the technical field of underwater image enhancement, and the method comprises the steps: taking an original underwater image and a corresponding illumination priori image as input, obtaining illumination factor mapping through feature splicing, a first convolution layer, a multi-scale channel aggregation module layer and a second convolution layer, and obtaining an illumination priori image; performing Hadamard product operation on the illumination factor mapping and an original image to obtain a basic enhanced image; the multi-scale degradation features of the basic enhanced image are extracted step by step through an encoder, the spatial resolution is recovered through jump connection of a decoder, the illumination-texture relevance is enhanced through selective state space modeling of a WLMama bottleneck module, and finally a high-quality underwater image enhancement result is generated. According to the method, the mathematical representation system of the optical characteristics of the water body is established, the model can adaptively adjust the chromaticity compensation intensity, and image restoration under the constraint of the physical characteristics of the underwater scene is realized.

The invention belongs to the technical field of digital restoration of computer vision and cultural heritage, and particularly relates to a traditional picture restoration method fusing low-resolution prior and efficient visual selection, which comprises the following steps: constructing a multi-source image data set, taking images in the multi-source image data set as high-resolution images, preprocessing the high-resolution images to obtain low-resolution images, and carrying out high-resolution priori and high-efficiency visual selection on the low-resolution images. The high-resolution image and the low-resolution image are respectively masked to generate simulated damage mask images, and the simulated damage mask images comprise a regular damage mask image and an irregular damage mask image; taking the multi-source image data set and the preprocessed multi-source image data set as training data, and training a multi-source image model; the dual-stage repair network comprises a coarse repair network and a fine repair network; according to the method, the problems of structural semantic loss, high priori information dependency and insufficient global and local coordination when an existing image restoration method is used for processing a complex scene and a large-range missing region are solved.

The invention discloses a medical image super-resolution reconstruction method based on multi-level attention guidance, and the method comprises the following steps: S10, constructing a deep learning network model based on a generative adversarial network architecture, which comprises a generator and a discriminator; the generator is based on an improved U-Net architecture, a hierarchical attention module and a dual-path feature processing module are configured in an encoder and a decoder of the generator, the hierarchical attention module adopts different attention strategies according to network levels to consider structure and texture, and the dual-path feature processing module separates and processes low-frequency and high-frequency information; the generator further comprises a multi-level feature fusion module for integrating the multi-scale features of the decoder, and an attention guide up-sampling module for final enhancement and dimension raising. The discriminator adopts a spectrum normalization U-Net architecture and uses multi-scale features for matching; s20, training the network model by adopting a composite loss function comprising pixels, adversarial, perception and total variation loss; and S30, inputting the low-resolution image into the trained model, and outputting a high-resolution image. According to the method, through deep fusion of multi-level attention and multi-scale feature processing, the image restoration quality can be remarkably improved, the texture detail definition can be enhanced, the anatomical structure accuracy can be ensured, and the noise robustness can be improved.

The invention discloses a bad weather image restoration method based on a multi-modal state space model, and belongs to the technical field of computer vision. In order to solve the problem that an existing unified bad weather image restoration method has limitations in the aspects of global receptive field and computational efficiency, the unified restoration of various bad weather images is realized by designing a parallel multi-mode encoder and an adapter to generate comprehensive prompts containing degeneration semantics. Through parallel connection of a state space module and a local context sensing module of a double-attention mechanism, simultaneous capture of global long-range dependence and local detailed features is realized. Experiments show that the method is high in generalization ability, high in processing speed and low in calculation complexity, and can show good adaptive capacity on a plurality of bad weather image removal tasks.

The invention discloses an image restoration method, an image restoration device and a storage medium, which are used for improving the structure restoration precision and the detail restoration capability of image restoration. The method comprises the following steps: acquiring multi-dimensional inertial data in real time; performing frequency domain analysis on the multi-dimensional inertial data by adopting sliding window short-time Fourier transform to obtain vibration intensity; if the vibration intensity does not exceed the preset threshold value, acquiring an image; calculating a definition index of the image; determining whether the image is a blurred image according to a preset definition standard and the definition index of the image; if the image is judged to be a blurred image, dividing the blurred image into a motion blurred image and a focusing blurred image; respectively constructing point spread function models of the motion blurred image and the focusing blurred image; performing deconvolution processing or depth reconstruction on the blurred image through a point spread function model to obtain a clear image; and recalculating the definition index of the clear image, and if the definition index does not exceed the definition threshold, triggering reacquisition or switching the repair model to execute secondary repair.

The invention discloses a waste plastic classification method and system based on visual identification, and particularly relates to the technical field of plastic classification. Reflection suppression is realized through an industrial camera equipped with a polarization filter and a multi-angle cross polarization light source, and the structure and color characteristics of a shielded area are effectively restored by combining brightness equalization, reflection area detection and image restoration technologies; and then semantic segmentation and target classification are completed by using an improved deep neural network model, a control signal is generated based on an identification result, and an execution mechanism is driven to realize accurate sorting of multiple types of plastics, so that the identification robustness and the sorting efficiency of the system under a complex illumination condition are improved.

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 provides an object positioning and posture recognition method based on 3D vision. The method comprises the steps that 3D point cloud data and image data corresponding to the 3D point cloud data are collected; preprocessing the image data to obtain a quality evaluation result, and executing image restoration processing based on the quality evaluation result to generate optimized image data; inputting the optimized image data into a pre-constructed conditional diffusion model based on hierarchical feature guidance, and generating feature representation of the optimized image data; and inputting the feature representation of the optimized image data into a differential transformer network to obtain a position and attitude prediction result of the object. According to the invention, through innovative image quality evaluation based on dual representation interaction driving, a conditional diffusion model based on hierarchical feature guidance and the design of a differential transformer network, the precision and robustness of object positioning and posture recognition in a complex environment are improved.

The invention provides a picture editing method and system based on multi-modal condition adaptation, and the method comprises the steps: obtaining a first text vector: obtaining a picture description corresponding to a picture through processing, processing the picture description through an editor, and obtaining a first text vector; obtaining a first text vector based on the picture information; a second text vector acquisition step: processing the used editing instruction to obtain a second text vector based on the editing instruction; a fusion feature acquisition step: fusing the first text vector and the second text vector through weight to obtain a fusion feature; an image potential feature code acquisition step; in the image editing step, the injected condition information is received, meanwhile, the received potential features and potential noise of the image are denoised, and the image desired by the user is gradually generated in the iterative denoising process under the guidance of the received condition information; and an image restoration step. According to the invention, the stability, controllability and accuracy of image editing can be improved.

The invention discloses an improved underwater image enhancement method. The method comprises the following steps of lightweight downsampling and feature extraction; performing multi-stage attention enhancement and feature optimization; multi-path feature reconstruction is carried out; and performing multi-stage output fusion and image restoration. According to the method, a UNet + + model is introduced into the field of underwater image enhancement for the first time, standard convolution of a down-sampling layer is replaced by lightweight convolution, a three-stage attention mechanism of local noise suppression, space-channel collaborative optimization and global color cast compensation is cascaded after each layer of lightweight convolution, and a multi-level enhancement result is generated. And finally fusing and outputting through a weighted average strategy. And through lightweight convolution replacement, the model parameter quantity and the calculation complexity are remarkably reduced, and the calculation efficiency is improved. Through a three-stage attention mechanism, typical degradation problems of suspended particle noise, edge blur, color distortion and the like of the underwater image are accurately repaired. According to the method, the common artifact problems of light spots and the like and the excessive smoothness problem in the existing method are solved.

The invention relates to the technical field of image processing, in particular to an integrated image restoration method and system based on image mask modeling, and the method comprises the following steps: collecting the boundary data of the whole image, separating the boundary of a mask region from the boundary of a non-mask region, and analyzing the gradient change characteristics of the boundary of the mask region. And extracting local depth information of the mask region from the gradient change features, and constructing a mask region boundary residual distribution model. According to the method, the mask region and the non-mask region are separated, the boundary gradient features are extracted, the residual distribution model is established on the basis of gradient change, the subtle change of the boundary of the mask region is accurately captured, and the accuracy of the boundary features is improved. By constructing a depth information data set and combining local depth information with gradient features, adjustment of layered dynamic weight distribution of a mask area basic structure is realized, effective extension of area features to the inside is ensured, and the possibility of boundary information loss is reduced.

The invention discloses a bronze ware ornamentation and pattern digital restoration method based on an image enhancement technology, and relates to the technical field of image restoration, and the method comprises the steps: building a space mapping matrix; extracting multi-modal data features by using the surface state of the topological insulator, and registering a joint data volume; forming a super-resolution image through super-resolution reconstruction; obtaining a material degradation coefficient by using a wavelet finite element method and a graph neural network; generating an adversarial network by utilizing physical constraints, and generating an embarrassment repairing result; a material sensing three-dimensional model is constructed by adopting a wavelet packet decomposition and neural radiation field fusion technology, texture mapping is dynamically adjusted based on a graphene Moire effect, and virtual-real fusion is performed through holographic waveguide AR; by combining advanced technologies such as a metamaterial lens, micro-distance laser scanning, a topological insulator film, a graphene heterojunction and a nerve radiation field, high-precision three-dimensional digital restoration and repair of bronze cultural relics are realized, and immersive augmented reality display experience is provided.

The invention discloses a lightweight super-resolution reconstruction method based on multi-scale feature extraction and parallel cavity coordinate attention. The lightweight super-resolution reconstruction method comprises the following steps: constructing a shallow feature extraction sub-network for remote sensing image super-resolution reconstruction; constructing a deep feature extraction sub-network for remote sensing image super-resolution reconstruction; constructing an image restoration and reconstruction self-sub-network for remote sensing image super-resolution reconstruction; constructing a remote sensing image super-resolution reconstruction network; generating a training set, a verification set and a test set; training a remote sensing image super-resolution reconstruction network; reconstructing remote sensing image resolution; according to the method, multi-scale feature extraction, a parallel cavity coordinate attention mechanism and depth separable convolution are combined, so that the effect and the quality of super-resolution reconstruction are improved while relatively low calculation cost is kept; the problems of detail loss, high calculation complexity, insufficient utilization of multi-scale features and the like when complex textures and high-frequency details are processed in an existing reconstruction technology are solved.

The present invention relates to the technical field of image restoration, and in particular to a remote-sensing hyperspectral image super-resolution reconstruction method based on a hypergraph neural network, comprising: S1, acquiring a hyperspectral image, and preprocessing the hyperspectral image to obtain a training set and a verification set; S2, constructing a hypergraph neural network; S3, using training images in the training set to construct a three-layer hypergraph, and on the basis of the three-layer hypergraph, constructing hypergraph operators corresponding to the training images; S4, using the training images and the hypergraph operators corresponding to the training images to train the hypergraph neural network, and using a loss function to iterate network parameters of the hypergraph neural network, to obtain a trained hypergraph neural network; and S5, inputting, to the trained hypergraph neural network, low-resolution hyperspectral images to be reconstructed for reconstruction to obtain a high-resolution hyperspectral image. The present invention has an excellent reconstruction result.

The invention belongs to the technical field of digital image processing, and particularly discloses a low-illumination image enhancement method for simulating a foggy day degradation mechanism, and the method comprises the following steps: generating a virtual haze image; estimating atmospheric light searched by combining decreasing cutting with a genetic algorithm; coarse transmissivity calculation based on dark channel prior; refining the transmissivity graph; carrying out defogging and low-illumination enhancement on the virtual haze image; and brightness and contrast self-adaptive compensation. According to the method, the precision and robustness of atmospheric light value estimation are improved, a transmissivity graph which better conforms to a scene can be obtained, and the image restoration effect is improved; the method has remarkable advantages in the aspects of reducing artifacts, enhancing image details, denoising, keeping structural information and restoring details.

The present invention discloses a rapid construction method and system for a wireless channel knowledge map. The present invention uses image restoration technology to construct a channel knowledge map, and, by means of recovering a corresponding pixel value in an image matrix, predicts channel knowledge of a specific position, so that channel related knowledge of a user equipment at any position in a target area can be acquired. By means of combining the similarity of an image restoration problem and a channel knowledge map construction problem, and using a Laplacian pyramid frequency band decomposition framework, an input environment map can be decomposed at different frequencies. According to the characteristics of different frequency components, a corresponding sub-network is designed for feature extraction, and finally, the channel knowledge map is reconstructed by means of inverse operation of the Laplacian pyramid. The wireless channel knowledge map construction method provided by the present invention not only implements higher construction precision at lower computational complexity, but also has strong generalization ability for various wireless communication scenarios.

The invention discloses a polarization image defogging method based on double iteration regular separation, and relates to the technical field of image defogging, and the method comprises the following steps: S1, obtaining a polarization image; s2, constructing a representation function of the fog concentration; s3, indirectly solving an intermediate variable T; s4, formula decomposition; s5, judging whether convergence occurs or not; and S6, finally defogging. When the polarization image defogging method based on double-iteration regular separation is used, non-uniformly distributed concentration characteristic differences are analyzed through a large number of polarization images in a non-uniform haze scene, on this basis, a dynamic fog concentration function in the image is effectively constructed in combination with polarization characteristics, estimation of an atmospheric scattering coefficient is achieved, and the defogging effect is improved. Meanwhile, a double-iteration separation algorithm is provided based on the atmospheric scattering coefficient, the optimal transmittance is solved, image sharpening restoration under the non-uniform haze scene is achieved, haze residues are further reduced, and the image restoration quality is also objectively improved.

The invention relates to the technical field of computer vision and artificial intelligence, in particular to an image restoration method based on automatic evaluation and dynamic optimization and related equipment. The method comprises the following steps: acquiring a to-be-restored image and a corresponding restoration area; performing restoration processing on the to-be-restored image to generate an initial restoration result; performing automatic quality evaluation on the initial repair result by using a multi-modal large model in combination with an anomaly detection rule base to obtain an evaluation result; based on the evaluation result, if it is judged that the repair quality does not reach the standard, an intelligent adjustment tool chain is called to carry out targeted optimization on the initial repair result, and an optimized repair result is generated; and repeatedly executing the quality evaluation and optimization steps on the optimized repair result until the repair quality reaches the standard, and forming a closed-loop processing flow. The method has the effects of improving the restoration quality in a complex scene in an image restoration technology, improving the image restoration efficiency and increasing an automatic closed-loop processing mechanism.

Disclosed are an image restoration method and device, and a non-transitory computer storage medium. The image restoration method includes: an original mask in an original to-be-restored image is processed into a dilated mask with a regular shape, the dilated mask is overlaid at the location of the original mask in the original to-be-restored image to acquire a mask enhancement feature map, and then the dilated mask in the mask enhancement feature map is restored using an image restoration network to acquire a restored image corresponding to the original to-be-restored image.

The invention discloses an image restoration method based on a gated Fourier convolution residual error and a multi-head attention mechanism, and the method is characterized in that the method specifically comprises the steps: collecting a damaged image as a data set, carrying out the preprocessing, and dividing the data set into a training set and a test set according to a set proportion; constructing an LGFCDANet network, wherein the network comprises a coarse recovery network and a fine recovery network; carrying out adversarial training on the LGFCDANet network to obtain a trained LGFCDANet network, and testing the trained LGFCDANet network by adopting a test set; and carrying out image restoration by adopting the trained network. The invention aims to enhance the understanding and recovery capability of the model on image details, thereby improving the restoration effect and enhancing the dynamic adjustment capability on the color and texture of a special area.

Disclosed in the present invention is a facial-image restoration method based on a state space model. The method comprises: inputting into a restoration model a facial image to be restored, so as to obtain a restored facial image, wherein the restoration model comprises an encoder and a decoder, the encoder sequentially comprising a first image fusion module, a first multi-scale state space module, a second image fusion module, a second multi-scale state space module, a third image fusion module and a third multi-scale state space module, and the decoder sequentially comprising a fourth multi-scale state space module, a first multi-scale attention fusion module, a fifth multi-scale state space module, a second multi-scale attention fusion module, a sixth multi-scale state space module and a third multi-scale attention fusion module. The method restores details and textures while ensuring the consistency of facial semantic information, and can achieve a better restoration effect particularly for a low-quality facial image in a real degradation scenario.

The invention discloses an optical fiber transmission image recovery method and device based on a complex amplitude regulation metasurface diffraction device, and the method comprises the steps: obtaining the complex amplitude distribution of each diffraction layer through the training of a diffraction neural network, designing a corresponding metasurface array, and preparing a cascaded metasurface diffraction device with a complex amplitude regulation function, deploying a cascade metasurface diffraction device to the far end of the multimode optical fiber and performing complex amplitude modulation on emergent circularly polarized light to realize high-resolution or high-sampling-rate image recovery; or an optical fiber transmission inverse matrix is solved by using an analytical method, and a corresponding metasurface array is designed, so that the single-layer metasurface diffraction device with a complex amplitude regulation and control function is prepared; and deploying the single-layer metasurface diffraction device to the far end of the few-mode optical fiber and carrying out complex amplitude modulation on emergent circularly polarized light to realize low-resolution or low-sampling-rate image recovery. Miniaturization of a multimode optical fiber imaging system can be realized, and the image recovery quality is effectively improved based on complex amplitude modulation of the metasurface diffraction device.

The invention provides an image snow removal method based on dual-frequency domain enhancement, which adopts a discrete wavelet transform technology to decompose high and low frequencies and fuse high-frequency enhancement features and low-frequency enhancement features after the high and low frequencies pass through a feature enhancement network so as to enhance the recovery of detail textures of a snowflake-degraded image. According to the method provided by the invention, the high-frequency characteristics of the snow degradation image are enhanced by using a differential convolutional network, and meanwhile, the serious degradation influence caused by large-scale snowflakes is reduced by using a low-frequency enhancement network, so that the performance of the snow degradation image in different frequency domains during snowflake removal and image restoration is improved. And meanwhile, global modeling is carried out on a seriously degraded area through a degradation sensing routing attention module, so that seriously degraded snowflakes are further removed more thoroughly.

The invention relates to a hyperspectral image restoration method and device based on a cyclic decoupling model. The method comprises the following steps: constructing a hyperspectral image hybrid degradation mathematical model according to pre-introduced descriptive factors; deducing a plurality of solving targets by using the hyperspectral image mixed degradation mathematical model; constructing a hyperspectral image restoration network with a cyclic decoupling structure, designing a plurality of sub-loss functions based on the plurality of solving targets, and training the hyperspectral image restoration network according to the plurality of sub-loss functions to obtain a trained hyperspectral image restoration network; and restoring an image to be restored according to the trained hyperspectral image restoration network. By adopting the method, the denoising and defect information complementation of the hyperspectral image can be realized at the same time, and the image quality is greatly improved.

The invention provides an image restoration method and system based on dynamic decomposition and fusion, and the method comprises the steps: decomposing a to-be-restored image with hybrid features according to a degradation mode, and extracting degradation features of which the number is the same as the type number of the degradation mode; respectively processing each degradation feature to obtain a recovery feature corresponding to each degradation mode; and the recovery features are dynamically fused to obtain a clear recovery image with the same size as the to-be-recovered image, so that the function of performing high-quality recovery on the complex hybrid image is realized. According to the method, the recovery feature map is decomposed, processed and fused under multiple scales, the recovery feature map fusing the recovery features of the multiple scales is obtained, and the visual effect of hybrid image recovery is improved. According to the method, the symmetric U-shaped network is constructed based on the corresponding model formed by the method, and the processing sub-module corresponding to the degradation mode is constructed, so that complex and diversified degraded images in a real environment are well dealt with, and an efficient and reliable solution is provided for the field of image restoration.

The invention provides a training method of a generative denoising device and an image controllable generation and restoration method. The training method comprises the following steps: taking a pre-trained diffusion model as a teacher diffusion model; initializing a generative denoising device, a score model and a discriminator; obtaining a noiseless signal, and obtaining a noisy signal and Gaussian noise in combination with a generative denoising device; obtaining effective noise intensity and effective noise signals according to the noisy signals and the Gaussian noise; estimating a data score by using a teacher diffusion model according to the effective noise intensity, the effective noise signal and the noisy signal; according to the noisy signal, estimating a model score by using a score model; calculating and optimizing the gradient of learnable parameters of the generative denoising device; and according to the noisy signal, the data score, the model score and the optimized generative de-noising device, calculating de-noising score matching of the score model and adversarial loss of the discriminator, and optimizing the score model and the discriminator. And realizing image controllable generation and image restoration based on the generative de-noising device.

The embodiment of the invention provides an image restoration method and device, electronic equipment and a storage medium, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a target detection frame and a redrawn mask of a to-be-restored image; cutting the to-be-restored image according to the target detection frame to obtain a local image; generating a guide vector according to the local image; inputting the guide vector and the redrawn mask into a diffusion model to obtain a target feature; and inputting the target feature into an image decoder for decoding to obtain a redrawing image. According to the embodiment of the invention, the distorted hand region in the portrait can be effectively repaired, the image quality is improved, the style consistency of the repair result is kept, and the repair effect is improved.

The invention discloses an image restoration method based on a multi-scale residual module and feature fusion, and belongs to the technical field of image processing. The multi-scale residual module designed by the invention can effectively capture feature information of different scales of the image, the quality of the repaired image is improved from a fine texture to a macroscopic structure, a feature fusion strategy fusing detail branches and semantic branches can fully integrate features of different levels, the detail expression and overall coordination of the image are enhanced, and the image quality is improved. Through fusion of multi-scale features, limitation possibly brought by single-scale features is avoided, and the repaired image is more real and fine in color, texture and structure. And the multi-scale residual error module can relieve the gradient disappearance problem, the calculation efficiency is high, the image restoration task can be completed in a short time, and the introduced and designed dual-channel global attention module has good robustness, so that the method can stably play a role in various complex image damage conditions.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.