258 results about "Image recovery" patented technology

The invention discloses an image deblurring system and method based on Transform and multi-scale feature fusion, and relates to the technical field of image processing, the system comprises an input layer, a processing layer and an output layer, the processing layer comprises a multi-input multi-output encoder-decoder module, the multi-input multi-output encoder-decoder module is used for extracting features from blurred images of different scales through an encoder, and outputting the features to the output layer; a clear image is gradually restored based on the extracted features through a decoder; the multi-feature multi-scale fusion module is used for integrating local and global feature information and enhancing the learning ability of the system to global features; according to the improved Transform module, a self-adaptive window mechanism and a cross-channel attention mechanism are introduced, so that the system can better adapt to different types of blurred images, and the effects of feature extraction and image restoration are improved; and the attention fusion module dynamically learns and integrates useful feature information by introducing a channel attention mechanism. The image deblurring method is suitable for various image deblurring application scenes, and image detail features can be effectively recovered.

The invention discloses a low-light image enhancement method and system, relates to the field of computer vision, and solves the problems of non-uniform illumination distribution, detail loss, color distortion and the like in an image recovery process in an illumination mapping technology in the prior art. The method comprises the steps that a training set and a test set are acquired, a low-light image is subjected to wavelet transformation to obtain a low-frequency image sub-band of the low-light image and different high-frequency image sub-bands in the horizontal direction, the vertical direction and the diagonal direction, and the low-frequency image sub-bands and the high-frequency image sub-bands are obtained through enhancement based on a feature extraction network model; the enhanced image is obtained through enhancement based on a low-frequency enhancement network model, and a final enhanced image is obtained through processing of a plurality of hierarchical feature extraction structure units; respectively training the overall hierarchical network model by using a loss function; and forming a sample pair by the low-illumination image in the test set and the normal-illumination image corresponding to the low-illumination image, and repeating the operation to obtain a final enhanced image. The method is applied to the fields of security monitoring and the like.

The invention discloses a multi-weather degraded image restoration method based on a state space model. The method comprises the following steps: acquiring multi-scene clear images, synthesizing images under rain, snow and fog weather conditions by using an image synthesis technology, and constructing a standard multi-weather data set; preprocessing the data set and then dividing the data set; constructing a multi-weather degradation image restoration model based on the state space model, and performing training optimization on the model; the method comprises the following steps: acquiring an image which is degraded due to weather influence, preprocessing the image, inputting the processed image into a pre-trained multi-weather degraded image restoration model for restoration, and outputting the image as a processed clear image. According to the method, a self-adaptive channel attention mechanism is introduced based on the state space model, the capturing capability of the network on image local features is enhanced, images of various scales can be effectively processed through overlapped block embedding and multi-scale processing strategies, and the image recovery performance is remarkably improved. The method provided by the invention can process image restoration tasks including but not limited to various weather degradation caused by rain, snow and fog, and has a wide application prospect.

The invention relates to the field of image processing, in particular to a multi-modal guided progressive image generation method, which comprises the following steps of: generating multi-modal features for received data; generating a final layer layout for each main object by using a layout denoising network through a noise sampling and multi-modal feature fusion method; extracting potential codes of a reference image through an inversion algorithm, combining layer text codes, performing iteration denoising through a stable diffusion model, and extracting a reference key value matrix; the initial noise, the final layer layout, the multi-modal features and the reference key value matrix are input into a layered diffusion model, and after multi-step denoising, a main body transparent image layer is generated through decoding; and after all the main body transparent layers are superposed to form a mixed image, driving the image recovery network to generate a target image based on global text coding. According to the method, the defects of an existing method in processing a complex scene are overcome; and the generated image is ensured to be consistent with the text description globally and locally.

The present application provides a cardiac fibrosis diagnosis model based on multi-task attentional feature fusion. The cardiac fibrosis diagnosis model is established by the following steps: S01: image collection and labeling: obtaining cardiac magnetic resonance (MR) images as sample data, and performing manual labeling to obtain heart labels corresponding to the MR images; S02: image preprocessing, including normalization processing, data enhancement, and data clipping; S03: model establishment, including establishment of an image recovery network and establishment of an image segmentation and classification network, and executing an image recovery task; S04: model pre-training: training the image recovery network such that the encoder of the image recovery network fully learns the feature of the cardiac fibrosis image; and S05: model training. An objective of the present application is to improve the segmentation precision and diagnosis accuracy of a network model for a cardiac fibrosis image.

The invention discloses a single-bit image recovery method and device, a terminal and a medium, and relates to the field of image processing. The method comprises the following steps: performing single-bit quantization on a to-be-recovered image by adopting a non-zero quantization threshold, and determining an initial image quantization model; replacing a sign function in the initial image quantization model with a smooth hyperbolic tangent function, performing low-rank matrix decomposition on the to-be-recovered image, and determining a target image quantization model; determining an initial optimization model according to the truncated least square function and the target image quantization model; a semi-quadratic minimization method is adopted to convert the initial optimization model, and a target optimization model is determined; solving the target optimization model by adopting a near-end block coordinate descent algorithm to obtain each low-dimension sub-matrix and a noise matrix; and determining a reconstructed image by using each low-dimensional sub-matrix. The problem that in the prior art, quantization noise cannot be effectively restrained, and therefore image restoration precision is affected is effectively solved.

The invention relates to an image steganography method based on a high-frequency distribution mapping depth reversible network, and the method comprises the steps: designing a reversible steganography module and a high-frequency distribution mapping module based on an affine coupling structure, and constructing a depth reversible steganography network; combining the forward hidden network and the reverse recovery network, constructing a corresponding loss function, and training an image steganography process, an image recovery process and a high-frequency distribution mapping process for the deep reversible steganography network to obtain a trained deep reversible steganography network; and on the basis of the trained deep reversible steganography network, image steganography, high-frequency distribution mapping and image recovery processing are sequentially carried out on the current carrier image and the secret image, and a secret-carrying image and a recovered image are respectively obtained. Compared with the prior art, the method has the advantages that the high-frequency distribution mapping module is designed to improve the recovery quality of the secret image in the reversible neural network, global end-to-end image steganography is realized, and the image steganography safety, the recovery image quality and the steganalysis resistance can be effectively improved.

The invention discloses a semantic intelligent enhanced DNA storage method for the Internet of Things, and belongs to the crossing field of artificial intelligence and biological information storage, the compression efficiency and robustness of DNA image storage are effectively improved through introduced semantic extraction and a multi-read screening mechanism of design, key information areas in images are extracted through semantic extraction, and the compression efficiency and robustness of DNA image storage are improved. The method has the advantages that redundant backgrounds are eliminated, coding lengths are reduced, high-quality sequences are selected from sequencing copies by the aid of scoring and sequence analysis through a multi-read screening mechanism, fault tolerance is enhanced, the method is applicable to efficient semantic storage in scenes of the internet of things, the problem of low storage efficiency due to excessive redundant information in original images in existing DNA image storage methods is solved, and the method is applicable to high-efficiency semantic storage in scenes of the internet of things. And a high bit error rate exists in the DNA synthesis and sequencing process, so that the image recovery quality is influenced.

The invention discloses a medical image reconstruction method and device, and belongs to the technical field of image processing, and the method can achieve the cooperative work of an overall model through a fine-tuning visual language model, a feature adapter module and an image restoration generator, and achieves the reconstruction of a single-channel to-be-processed medical image and corresponding modal text prompt information. A complete process from feature extraction, fusion to reconstruction is formed, so that the image quality of the reconstructed medical image can be improved, and the pertinence and effectiveness of medical image reconstruction are improved.

The invention provides an image shadow removing method and system based on image filling enhancement, and relates to the field of image processing, and the method comprises the following steps: constructing an image restoration model; according to a real image of the shadow-removed data set, constructing an image filling task training data set by using a random mask, training an image restoration model by using the image filling task training data set, and storing a model weight file; based on the model weight file, training the trained image recovery model again, optimizing the image recovery model in the training process, and determining and storing a model optimization weight file according to the optimized image recovery model; and carrying out initialization processing on the image restoration model, and carrying out shadow removal processing on the to-be-processed image with the shadow by using the initialized image restoration model to obtain the image with the shadow removed. According to the method, the dynamic adaptation of the task is realized, and the processing capability of the model to cope with a complex degraded scene is remarkably improved.

The invention discloses a double-view-based deep enhancement CT bone imaging method and system, high-quality image recovery and bone microstructure analysis are realized by using deep learning and multi-strategy fusion technologies, and the system comprises the following modules: a Cascade-SwinUNETR trunk module used for feature extraction and enhancement and realizing efficient multi-scale feature aggregation; and the double-view feature fusion module is used for extracting and integrating complementary information by combining CT image data from different visual angles, so that the detail representation and the structural accuracy of the image are improved. And the unsupervised domain adaptation module (UDA) is used for adapting to different data distributions, does not need additional annotation, realizes cross-domain learning and model generalization, and ensures the stable performance of the system in various medical scenes. According to the method, by improving the definition and accuracy of CT imaging, the decision-making quality and efficiency of doctors in bone health assessment can be remarkably improved, and meanwhile dependence on invasive sampling is reduced.

The invention discloses a step refractive index fiber endoscopic bending microscopic imaging loss recovery method and device. The method comprises the following steps: s1, searching and removing an image loss part in a step refractive index fiber endoscope scanning image; s2, image restoration: carrying out feature matching on the two displacement pictures processed in the step s1 to calculate x-direction and y-direction displacements of the two images, and then aligning and fusing the two images in a Poisson fusion mode to repair image loss parts; and s3, background denoising: estimating transmissivity by calculating a dark channel and a background illumination value, and denoising the image according to the transmissivity to obtain a denoised image. According to the method, through frequency domain low-pass filtering and feature matching fusion, lost image center information is successfully recovered, image center details are effectively recovered, and image recovery can be realized through processing of an imaged image.

The invention belongs to the technical field of multi-modal driving video analysis methods, and particularly relates to an image-enhanced multi-modal driving video analysis method, which comprises the following steps of: pre-training an image, converting a clear image and a degraded image into potential space representation, adding noise to the clear image by using a diffusion probability model, and guiding a recovery task through task prompt. Meanwhile, the model is finely adjusted by using Contro Net, alignment of potential space representation and task prompt is ensured in cooperation with a self-attention and text cross attention mechanism, the difference between an image and a target image is recovered through L2 loss minimization, training of an image recovery model is completed, key frame extraction, target detection and three-dimensional positioning are performed on an enhanced video, and the image recovery efficiency is improved. The method comprises the following steps: constructing semantic text description, carrying out multi-modal coding through a visual language model, generating structured perception representation, carrying out structured spatial reasoning by utilizing a small-scale language model, and training the model by adopting a strategy optimization algorithm, so that the model generates a reasoning process and an accurate answer under the condition of inputting scene description and questions.

The invention discloses an ultra-high-definition image recovery method based on a frequency-enhanced variational auto-encoder, which is characterized in that a Fourier-based lightweight frequency learning module is designed to improve parameter efficiency, a wavelet-based adapter is introduced to extract multi-scale image information, a bridging domain gap is adaptively modulated through frequency perception, and the ultra-high-definition image is recovered. And injecting the high frequency information into a VAE decoder to enhance details of the restored image. The invention aims to solve the problem of consistency caused by down-sampling in the existing UHD image restoration method, overcomes challenges of VAE (variational auto-encoder) in UHD image restoration by introducing frequency priori by utilizing strong potential space representation and reconstruction capabilities of the VAE, including the problems of large parameter quantity, domain gap, high-frequency information loss and the like, and improves the UHD image restoration efficiency. Therefore, the image restoration effect with higher quality is realized.

The invention relates to a BMC firmware mirror image recovery method and device and terminal device.The method comprises the steps that a storage area of a preset storage chip is divided into multiple areas, and the multiple areas comprise a first area and a second area; a first file in the firmware mirror image file is coded through a forward error correction algorithm, redundant recovery data is obtained, backup files of second files except the first file in the firmware mirror image file are obtained, and the first file comprises a root file system; the first file and the redundancy recovery data are stored in the first area, and the second file and a backup file of the second file are stored in the second area; when it is detected that the first file is abnormal, the first file is recovered based on the redundant recovery data through a forward error correction algorithm; and when detecting that the second file is abnormal, recovering the second file through the backup file of the second file. According to the method and the device, the problem that the reliability of recovering the firmware mirror image file through out-of-band network transmission is relatively low is solved.

The invention provides a magnetic resonance image reconstruction method and device, an imaging system and a storage medium, and the method comprises the steps: inputting to-be-reconstructed undersampling K space data into an image reconstruction model, and obtaining a reconstructed image; and the image reconstruction model is trained by taking model training loss value minimization as a target: performing inverse Fourier transform and down-sampling on the full-sampling K-space data to obtain a full-sampling image and under-sampling K-space data, and performing randomization, interpolation and inverse Fourier transform on the under-sampling K-space data in sequence to obtain a frequency domain training image; and fusing an undersampling training image obtained by inverse Fourier transform of the undersampling K space data and an undersampling image obtained by sequentially carrying out multiple times of second downsampling and interpolation on the undersampling training image to obtain an image domain training image, and calculating a loss value of the training image obtained by fusing the frequency domain and the image domain and the full-sampling image. According to the invention, through adaptive filling and reconstruction of the image domain and the frequency domain, efficient, rapid and high-quality magnetic resonance image recovery is realized.

The invention discloses a remote sensing image super-resolution reconstruction method based on a diffusion bridge model. The method comprises the following steps: firstly, constructing a diffusion bridge model, and connecting low-resolution and high-resolution image endpoints; secondly, explicitly aligning states of an image recovery bridge process and a pre-training generation diffusion process by designing a state alignment mechanism so as to multiplex powerful generation prior of a pre-training model; meanwhile, a lightweight feature adapter is introduced into a decoding end of the pre-training model, and high-frequency detail features reserved by an encoder are injected into a decoding path through a gating residual mechanism so as to suppress an over-smoothing phenomenon caused by VAE decoding. According to the method, the problems of insufficient prior utilization and high-frequency detail loss of a traditional diffusion model in remote sensing image super-resolution reconstruction are effectively solved, the structural integrity and visual fidelity of the image are ensured while the reconstruction efficiency is remarkably improved, and the method is suitable for a multi-scene remote sensing image super-resolution task.

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 relates to the technical field of computer vision and digital image processing, and discloses a two-stage damaged face image restoration method based on a generative adversarial network, and the method comprises the following steps: inputting a damaged face image into a reconstruction network, the input feature map is processed through at least one self-adaptive harmonic purification convolution module built in the reconstruction network, and a purified feature map is generated; based on the purified feature map, the reconstruction network outputs a low-frequency basic image and a final degradation feature map; inputting the low-frequency basic image and the final degradation feature map into a detail generator to generate a high-frequency detail map; and fusing the low-frequency basic image and the high-frequency detail image to obtain a final restored image. According to the method, the adaptive harmonic purification convolution module is arranged in the reconstruction network, frequency domain analysis can be carried out on local image features in the initial stage of feature extraction, and the harmonic attention mask is adaptively generated according to the content.

The invention discloses a two-stage multi-task image restoration method based on an RDM-CS framework (see figure 1), which can process various image restoration tasks such as low illumination, rain removal, snow removal, defogging and the like at the same time. Comprising the following steps: 1) processing and dividing a data set; and 2) carrying out feature extraction by using a VQGAN encoder and completing forward diffusion on the feature zt. And 3) training the prediction noise network and the prediction residual network to obtain residual and noise, and then completing reverse generation. And (4) training a CS (Channel-Spatial Transform) network (Channel-Spatial Transform). And 5) testing in the test set of each data set, and evaluating the image restoration effect. According to the method, through the multi-stage feature extraction and diffusion process, the model can effectively process various image degradation problems, and the image restoration quality and efficiency are improved. The system has a unified model architecture, can adapt to different image recovery tasks, and has relatively high practicability and popularization value.

The invention discloses an underwater concrete structure apparent defect detection method based on a Dark-Retinex model. The method comprises the following steps: acquiring an original image of an underwater concrete structure; processing the original image by using a dark channel defogging algorithm and a Retinex defogging algorithm to obtain a DR image; converting the DR image into a grayscale image and mapping a brightness value of the grayscale image to a new value image; carrying out image binarization on the new value image and reversing the image, so that white pixels in a binary image represent defect parts; marking a connected domain to remove interference information; and extracting apparent characteristic parameters of a white area in the binary image. The method is an image enhancement Dark-Retinex algorithm fusing a dark channel algorithm and a Retinex algorithm, image recovery of underwater concrete structure apparent defects and defect parameter quantitative evaluation are realized, and the apparent defect evaluation precision of underwater concrete structure images is improved.

The invention relates to the technical field of image deblurring and the like, in particular to a lightweight deblurring method based on frequency domain and spatial domain feature interaction, which comprises the following steps of: acquiring low-level feature embedding of an image to be restored by adopting a 1 * 1 convolutional layer; low-level features are embedded and converted into deep-level features through a U-shaped network formed by a multi-level symmetric encoder-decoder, and each level of the multi-level symmetric encoder-decoder is formed by a plurality of FSTBlocks; carrying out refining processing on the deep features under high spatial resolution, and generating a residual image for the refined features by applying a convolutional layer; and adding the residual image and the to-be-restored image to obtain a final restored image. While efficient image recovery is realized, the lightweight of the network is successfully realized, the calculation efficiency and deployment feasibility are greatly improved, and the method has the advantages in practical application.

The invention discloses a face image restoration method and system based on a mask auto-encoder, and belongs to the technical field of deep forgery detection, and the method comprises the steps: employing a RetinaFace model to cut a forgery face image, and obtaining a source face image and a target face image corresponding to the forgery face image; extracting features of the source face image, performing up-sampling on high-level semantic features, then splicing the high-level semantic features with shading features, and obtaining a segmentation probability graph based on a spliced feature graph; multiplying the segmentation probability graph by the source face image to obtain source face information, and restoring and reconstructing the source face based on the source face information to obtain a reconstructed source face; partitioning the segmentation probability graph, calculating the mean value of each segmentation probability graph, calculating the weight based on the mean value, and performing masking based on the weight to obtain a covered image; and inputting the potential target face features in the reconstructed source face and the covering image into a mask auto-encoder to obtain a face image recovery image of the forged face image.

The invention provides a target identification method and system for a severe working condition environment, and the method comprises the steps: firstly obtaining sequence image data, which are collected in a natural gas purification plant inspection region under the conditions of dust interference, smoke shielding and illumination variation, and comprise equipment, pipeline and instrument region images; analyzing the interference type and degree through a working condition interference sensing module, generating interference sensing parameters, inputting the interference sensing parameters into an image recovery parameter generation module, generating an image recovery parameter set, and performing layered recovery on the sequence image data according to the parameter set by utilizing an image recovery module, so as to eliminate various interference influences; and finally, performing feature extraction and classification identification on the recovered image data through a target identification and state judgment module, and generating an inspection target identification result containing a target area equipment type, an operation state identifier and a spatial position coordinate, thereby effectively coping with severe working conditions and improving the target identification accuracy.

The invention discloses a generative adversarial network (GAN)-based recoverable watermarking resisting method, which not only can ensure that an image is protected by privacy and copyright, but also can only allow an authorized party to safely use. Specifically, an encoder-decoder-restorer architecture based on a GAN (Generic Area Network) is innovatively designed, and watermarking-resistant embedding and extraction and image restoration are established as a unified task. And a dual optimization strategy is designed to resist loss and a dynamic joint training strategy, so as to achieve ideal balance among image quality, copyright protection, privacy protection and image recovery capability. Experimental results show that the method provided by the invention can ensure that the authorized DNN classifier recovers the protected image to perform accurate classification and identification when needed, and meanwhile, excellent privacy and copyright protection capability is also provided.

The invention discloses a hyperspectral image restoration method integrating sparse and low-rank prior. The method comprises the following steps: identifying image information; inputting codes into an optimization model: extracting image features, inputting the extracted features into an encoder, arranging the encoded features into an observation matrix, and inputting the observation matrix into the optimization model to obtain an optimization model; the optimization model is used for decomposing the observation matrix into a low-rank matrix L for capturing a data basic structure and a sparse matrix S for capturing an abnormal value; calculating and recovering by using an algorithm; and output image recovery: analyzing the noisy hyperspectral image data and decomposing the noisy hyperspectral image data to obtain a low-rank underlying structure L and a sparse anomaly S, then respectively recovering a low-rank part L and a sparse part S, and finally combining the low-rank part L and the sparse part S to generate a final recovery result, namely the processed image. The method not only can better process a complex data set, but also can improve the quality of image restoration.

The invention discloses an underwater image restoration method based on polarization scattering and deep learning, which is characterized in that background region scattered light of an underwater image is removed based on a polarization scattering physical model, the color of the image is restored by utilizing machine learning, and the images of preliminary restoration and secondary restoration are fused to obtain a final image. And a final underwater image recovery image is obtained. Compared with the prior art, the method has the advantages that a mixed underwater image recovery algorithm is formed by combining the advantages of a polarization scattering model and deep learning on the basis of machine enhanced vision, the fidelity is effectively enhanced, detail information is recovered, and the problem of accuracy pixel-level matching of machine learning data set acquisition is effectively solved; the comprehensive application problems of large color loss, high scene demand, high recovery upper limit of machine learning and difficult model training of a physical model are solved, the method is simple and convenient, the use effect is good, and the method has good application prospects and commercial development value.

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 provides an underwater optical image real-time enhancement method, system and device and a storage medium, and relates to the technical field of image processing and recognition. An isomorphic multi-stage image enhancement network is constructed, the image enhancement network comprises four stages, four branches with different feature scales are obtained through down-sampling operation in each stage, and after each branch is connected with a corresponding star convolution module for feature extraction, the features are input into a multi-scale fusion module; after fusion, after extraction of a pixel attention mechanism module, a convolutional layer is spliced to output an enhanced image, progressive enhancement is realized through a cross-stage feature feedback mechanism, the enhanced image and features output by the later stage and the previous stage are used as input, underwater image recovery is performed, and multi-scale feature extraction is adopted to obtain an underwater image. The calculation complexity is remarkably reduced, the network degradation phenomenon caused by multi-stage training is avoided, and the reliability of efficient detection and maintenance of the hydropower station is improved.

The invention provides an image restoration method and device, electronic equipment and a storage medium, and relates to the field of computer vision. The method comprises the steps that a training image set is acquired, the training image set comprises a plurality of training samples corresponding to various severe weathers, and each training sample comprises a training image collected in the severe weathers and a clean reference image corresponding to the training image; and training a neural network model by using the training image set until a training stop condition is satisfied, and obtaining an image recovery model which is used for recovering an image collected in severe weather. When the neural network model is trained, a weather prompt is determined according to the weather degradation feature prompt pool, a scene prompt is determined according to the scene features, then a comparison prompt loss value is determined, finally the total loss value of the neural network model is determined, model training is performed according to the total loss value, and then an image restoration model is obtained. The image restoration model can cope with various weather degradation combinations, and the accuracy of image restoration is improved.