4742 results about "Low resolution" patented technology

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

The invention relates to the technical field of image processing, and particularly discloses a remote sensing image super-resolution reconstruction method and system based on a prior diffusion model, and the method comprises the steps: carrying out the shallow feature information extraction of a low-resolution image, and obtaining a shallow feature map; the method comprises the following steps: performing multi-scale down-sampling and convolution fusion processing on a shallow feature map and a high-resolution image, and performing high-frequency information extraction and adaptive high-frequency enhancement processing on the processed feature map to obtain condition information; according to the method, deep mining of high-frequency information is carried out on a low-resolution image input as condition information, so that the high-frequency information in the low-resolution image gradually approaches a high-resolution image, and the reconstruction precision is improved; the noise prediction network model guides step-by-step noise removal in the back diffusion process, and the detail reduction capability and precision of natural disaster remote sensing image super-resolution reconstruction are improved.

The invention discloses a high-resolution remote sensing image semantic segmentation model and segmentation method, and relates to the technical field of remote sensing image processing, and the model comprises a feature representation and preliminary fusion coding module, a deep feature processing and fusion coding module, and a decoding and feature reconstruction module. A self-adaptive weighted fusion module is introduced into the feature representation and preliminary fusion coding module, and effective fusion of multi-modal data is realized by dynamically adjusting weights of different modal features; the deep feature processing and fusion coding module realizes gradual enhancement of features and deep fusion of cross-modal information through a multi-level sparse attention mechanism and a gating fusion mechanism combined with attention; and the decoding and feature reconstruction module recovers the low-resolution feature map generated by the encoder to the high resolution of the input image through step-by-step up-sampling and multi-scale feature fusion. The method is superior to the prior art in the aspects of calculation efficiency, segmentation precision and robustness for processing complex scenes.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention relates to the technical field of image processing, and discloses a lightweight image super-resolution reconstruction method based on frequency domain-spatial domain assisted Mama, which comprises the following steps: acquiring an original image data set and carrying out data preprocessing on the original image data set to acquire a training data set and a test data set; introducing a frequency domain-spatial domain Mama layer and an interaction layer based on transpose and self-attention features, and constructing a lightweight image super-resolution reconstruction model based on frequency domain-spatial domain assisted Mama; inputting the training data set into a lightweight image super-resolution reconstruction model based on frequency domain-spatial domain assisted Mama for training to obtain a trained lightweight image super-resolution reconstruction model with weight based on frequency domain-spatial domain assisted Mama for reconstructing a low-resolution image into a high-resolution image; inputting the test data set into a trained lightweight image super-resolution reconstruction model with weight and based on frequency domain-space domain assisted Mama to obtain a reconstructed high-resolution visual image; according to the method, lightweight image super-resolution is realized.

The invention discloses an image super-resolution reconstruction method based on deep learning, and belongs to the technical field of image signal processing. Comprising the following steps: S1, realizing multi-dimensional feature representation of a low-resolution input image by using a deep learning network; s2, realizing accurate alignment and lossless mapping of cross-scale features, and effectively solving the problems of feature dimension inconsistency and information distortion; s3, explicitly learning an increment mapping relation between low-resolution input and a reconstructed high-resolution image, and relieving the gradient disappearance problem of a deep learning network structure; s4, enhancing the naturalness and detail fidelity of the reconstructed image through adversarial training; s5, integrating pixel-level reconstruction loss, perception loss and structural similarity loss, and effectively balancing objective indexes and subjective visual quality of the reconstructed image; and S6, realizing joint optimization of deep learning network parameters through a unified gradient back propagation mechanism, and finally outputting a high-quality super-resolution reconstructed image.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

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.

A high-fidelity three-dimensional reconstruction method for a specular reflection plane comprises the steps of decomposing pixel colors into diffuse reflection and specular reflection components based on a 3D Gaussian sphere, introducing a dynamic reflection ratio parameter and a spherical harmonic function coefficient to respectively represent two reflection characteristics, and simulating light multi-reflection behaviors through weight fusion of cumulative projection and a reflection ratio map. Secondly, in combination with monocular inverse depth calibration, depth smoothing constraint of color gradient weighting and edge mutual exclusion loss, geometric consistency is enhanced, and artifacts are suppressed; a progressive multi-resolution training strategy is further adopted, low resolution is gradually optimized to complete resolution, reflection parameters are activated in stages, 3D Gaussian sphere overgrowth and floating artifacts are inhibited, and efficiency and precision are balanced. According to the method, the reconstruction fidelity of the specular reflection scene is remarkably improved while the real-time rendering advantage of the 3DGS is reserved, and the method is suitable for the high-precision modeling fields of virtual reality, augmented reality and the like.

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 medical image segmentation method based on high-resolution modal guidance and cross-modal boundary perception, and the method comprises the steps: carrying out the data preprocessing and enhancement of multi-contrast magnetic resonance imaging data, obtaining a boundary mask through a Canny operator and a Dilatation operation, constructing a multi-modal low-resolution data set, and carrying out the recognition of the multi-modal low-resolution data set; meanwhile, a high-resolution T2f modal data set is reserved, and the data set is divided into a training set, a verification set and a test set; a segmentation model is constructed, and the segmentation model comprises a high-resolution mode-guided double-encoder architecture module, a cross-level attention collaboration mechanism module, and a segmentation branch and boundary prediction branch decoder module; designing a training strategy of joint optimization of boundary contour detection and region segmentation, training the segmentation model by using a training set, and storing optimal model parameters on a verification set; and carrying out model performance verification in the test set, and segmenting a to-be-tested medical image by using the verified segmentation model.

The invention relates to the technical field of topographic mapping, in particular to a DEM super-resolution reconstruction method and system combined with a depth map, and the method comprises the steps: constructing a multi-modal data set which comprises an optical image, a depth map, a low-resolution DEM image and a high-resolution DEM image which are paired; designing a multi-modal fusion super-resolution network; designing a pseudo Siamese feature extraction module to extract low-level features and high-level features of the optical image, the depth map and the low-resolution DEM image; fusing the low-layer feature map generated by the pseudo Siamese feature extraction module in a shallow layer through a multi-scale feature fusion module, and fusing the deep-layer feature map in a deep layer; outputting a high-resolution DEM image through a DEM reconstruction module; and training the multi-modal fusion super-resolution network by using the multi-modal data set and the collaborative loss function. According to the method, the depth map generated by the optical image is used as priori knowledge to guide the DEM super-resolution reconstruction task based on the basic depth large model, a complex fine adjustment process is not needed, and the DEM reconstruction precision and efficiency are improved.

The invention provides an image restoration and super-resolution reconstruction system and method based on deep learning, and belongs to the technical field of digital image processing. The invention aims to solve the problems of high calculation complexity and resource consumption, limitation of long sequence processing, high training difficulty and texture scene deficiency when a multi-scale residual network based on a Transform architecture is used for image resolution conversion. The reconstruction system comprises: an image preprocessing module performing window division and video memory optimization on an input low-resolution image; the multi-layer fusion network dynamically adjusts the characteristics of the low-resolution image, captures channel information in different scenes, performs interactive fusion, performs comparison supervision, establishes an information communication channel, dynamically adjusts and optimizes parameters through negative feedback, and obtains a super-resolution image. And the loss function module maximizes the similarity of the super-resolution image and the high-resolution image in the segmentation feature space to obtain a final super-resolution image.

The invention discloses a semantic segmentation method for a low-resolution road scene, and aims to solve the problems of difficulty in small target recognition, fuzzy details, texture information loss and the like existing in a low-resolution image in the conventional semantic segmentation technology. The method comprises the following steps: (1) collecting a low-resolution road scene image and a corresponding semantic tag; (2) constructing a semantic segmentation model consisting of an edge guidance module (BGM), a double-domain feature decomposer (DDFD), a domain alignment attention fusion module (DAAFM) and a double-layer attention context aggregation module (HACAM); (3) designing a joint loss function to carry out multi-scale supervision on semantic regions, edges and middle features; (4) carrying out model training by utilizing the road scene image; and (5) outputting a semantic segmentation result map and an edge prediction map. The boundary perception capability is enhanced by introducing learnable pixel difference convolution, the extraction precision of a small target and a global structure is improved by combining frequency domain and spatial domain feature alignment, and context semantic relationship expression is optimized by fusing a channel and a spatial attention mechanism. The method effectively improves the semantic segmentation precision and boundary restoration capability of the model in a low-resolution complex road environment, and is suitable for intelligent analysis tasks of road images in scenes of automatic driving, intelligent traffic, severe weather and the like.

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 relates to the technical field of forest fire recognition and early warning, in particular to a forest fire early warning method and system based on a super-resolution neural operator, and the method specifically comprises the following steps: extracting an initial feature map # imgabs1 # of a to-be-recognized low-resolution forest fire image # imgabs0 #; constructing a super-resolution feature capture network, three HAFM hierarchical attention fusion modules, an MSMO multi-scale mapping operator module, a super-resolution feature fusion module and a super-resolution image reconstruction module, inputting the initial feature map # imgabs2 # into the super-resolution feature capture network, and finally outputting a super-resolution image # imgabs3 #; and constructing a forest fire recognition module, inputting the super-resolution image # imgabs4 # into a ResNet-18 neural network model to obtain a recognition feature # imgabs5 #, finally performing full connection layer processing on the recognition feature # imgabs6 # to obtain a recognition result of the forest fire image, and judging whether to perform early warning or not according to the recognition result. According to the invention, the problem that a low-resolution fire image cannot be accurately identified and timely early warned in existing forest fire identification and detection can be solved.

The invention discloses a feature integration method for interactive convolution and dynamic focusing of infrared images. The feature integration method comprises the steps that infrared image pairs with different resolutions in various real scenes are obtained through an infrared camera; performing degradation preprocessing on a part of original high-resolution images to obtain low-quality high-resolution images to form a mixed low-resolution data set, and dividing the processed data set into a training set and a test set; constructing a double-layer feature extraction module for feature modeling; training a network by using the processed training set, and optimizing a loss function; and inputting a low-resolution infrared image into the trained network, and outputting a high-resolution reconstruction result. According to the method, local and global features are fused, so that the super-resolution reconstruction quality of the infrared image in complex scenes such as low contrast and fuzzy edges is remarkably improved, and meanwhile, relatively high calculation efficiency is kept.

The invention discloses a remote sensing image ship detection method based on SPD-YOLOv8, which belongs to the technical field of satellite remote sensing, is used for ship detection, and comprises the following steps: acquiring a satellite remote sensing image ship data set in an experimental sea area, cutting a remote sensing image, and performing radiation correction and geometric correction; dividing the data set into a training set, a test set and a verification set; performing image enhancement on the data set; an SPD-YOLOv8 ship target detection model is constructed; performing ship target detection by using the ship target detection model; parameter tuning and result evaluation are carried out; deploying the ship target detection model to a server; and abnormal behaviors and illegal activities are detected and early warned. Compared with the prior art, the method has the advantages that the detection effect of low-resolution images and small targets is improved, the accuracy, real-time performance and response speed of the detection model are improved, ship targets of different sizes and types can be learned and generalized more effectively, and the robustness and practicability of a detection algorithm are improved.

The invention discloses a face image super-resolution method and device of a diffusion model based on residual prediction, and relates to the field of image super-resolution, and the method comprises the steps: obtaining a to-be-reconstructed low-resolution image; a diffusion model based on residual prediction and a conditional noise predictor are constructed and trained, and a trained diffusion model and a trained conditional noise predictor are obtained; gaussian noise is sampled from standard Gaussian distribution to serve as residual noise images, a to-be-reconstructed low-resolution image, the residual noise image of each step and the corresponding step length are input into a trained conditional noise predictor, prediction noise of each step is obtained through prediction, and the low-resolution image is reconstructed. Iteratively executing a de-noising process of T steps through a reverse de-noising network in the trained diffusion model to obtain a restored residual image; and adding the restored residual image and the up-sampled low-resolution image to be reconstructed to obtain a restored high-resolution image. The image super-resolution method solves the problems that an existing image super-resolution method is complex in generation process and limited in detail recovery.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

The invention belongs to the technical field of remote sensing super-resolution images, and particularly relates to a remote sensing image super-resolution system and method based on an adaptive Mamba-attention network. Comprising a feature extraction module used for carrying out shallow feature extraction on an input low-resolution image to obtain shallow features; the multiple cascaded adaptive state space blocks are used for processing the shallow layer features to obtain reconstruction features; and the reconstruction module maps the reconstruction features to a target resolution space through sub-pixel rearrangement operation to obtain a high-resolution remote sensing image. High-frequency details and a low-frequency structure are cooperatively processed in a feature space by using the remote sensing frequency sensing modulation module, and high-resolution output is generated by combining sub-pixel rearrangement up-sampling, so that high-quality reconstruction of a complex remote sensing scene is realized.

The invention discloses a target detection method, system and equipment based on knowledge distillation and a medium, belongs to the technical field of computer vision, machine learning and artificial intelligence, and aims to solve the technical problem of how to overcome the limitation of the existing knowledge distillation technology in smart city target detection application. According to the technical scheme, the method comprises the steps of hierarchical attention fusion: in combination with local attention and global attention mechanisms, capturing detail information of a target through a high-resolution feature map, extracting overall features of the target through a low-resolution feature map, and carrying out hierarchical attention fusion; a final attention feature map is generated through a self-adaptive fusion strategy, and the focusing capability of a student model on target features is enhanced; carrying out weighted fusion on the fusion strategy in combination with a channel attention mechanism, and further enhancing the expression ability through a lightweight convolutional network, thereby improving the expression ability of a student model for target features; improving a distillation loss function; optimizing a training strategy; and network training.

The invention relates to the field of remote sensing image processing, and discloses a high-quality HRHS image generation method based on a double-subnetwork structure, and the method comprises the following steps: receiving an input low-resolution hyperspectral image and a panchromatic image, which are respectively used for providing spectral information and spatial information; performing noise filtering on the low-resolution hyperspectral image through an anti-noise pre-fusion sub-network, and generating a pre-fused hyperspectral image by using the correlation between the high spatial resolution information of the panchromatic image and the spectral characteristics of the low-resolution hyperspectral image; performing high-frequency detail reconstruction and enhancement on the pre-fused image based on a conditional diffusion model through a high-frequency enhancement sub-network; and fusing the low-frequency information of the pre-fused image and the high-frequency detail information generated by the high-frequency enhancement sub-network through wavelet inverse transformation, and outputting a final high-resolution hyperspectral image. According to the method, spectrum and space information can be effectively fused in various noise environments, and a high-resolution hyperspectral image with hyperspectral fidelity and space details is generated.

The invention discloses a metal structural part surface damage identification method based on machine vision, and belongs to the field of machine vision, and the method comprises the steps: obtaining reference image data with known damage features, carrying out the preprocessing, analyzing the change trend of a system detection state, and judging whether there is a deviation correction demand or not. And if the deviation exists, carrying out geometric correction processing on the lens distortion error to obtain a corrected reference image. Further separating the real change of the damage from the system deviation, and combining low-resolution and high-resolution detection to obtain the distribution data of the suspected damage area and the specific characteristic parameter data of the damage. According to the method, quantitative data of damage levels are obtained through automatic classification, detection differences among multiple devices are calibrated, visual presentation information of damage positions and levels is generated, and finally camera parameters and algorithm thresholds for subsequent detection are optimized and adjusted, so that high-precision damage detection and evaluation are realized.

The present application relates to the field of computer vision. Provided are a visual encoding method and apparatus, and a visual encoding model training method and apparatus, which are used for using the same visual encoding model to encode images of different resolutions, and are applied to encoding scenarios for images of more sizes. The visual encoding method comprises: first, acquiring an input image, wherein the input image may be a high-resolution image and may also be a low-resolution image; and then inputting the input image into a visual encoding model, so as to output visual encoding data, wherein the visual encoding model is used for dividing the input image into a plurality of image blocks according to positional embedding, extracting features from each image block, and outputting visual encoding data on the basis of the features of each image block and corresponding positional encoding, the positional embedding is obtained by means of adjusting initial positional embedding on the basis of the difference between the input image and a preset resolution, and the positional embedding may specifically comprise a matrix corresponding to the division of the input image

Approaches presented herein provide for visual localization by matching features of a query image with features obtained from representation of a three-dimensional (3D) environment. A model such as a neural radiance field (NeRF) can be trained to represent the 3D environment. When a query image is received, query features can be extracted at two different resolutions. A lower resolution set of query features can be compared against NeRF descriptor features for a set of training images, to narrow the search space by finding a set of coarse matches. Higher resolution query features can then be compared against sampled features of these coarse matches, to identify 2D-3D correspondences that can be used to calculate camera pose information for the query image.

Systems and methods for upsampling low-resolution content within a high-resolution image include obtaining a composite image and a mask. The composite image includes a high-resolution region and a low-resolution region. An upsampling network identifies the low-resolution region of the composite image based on the mask and generates an upsampled composite image based on the composite image and the mask. The upsampled composite image comprises higher frequency details in the low-resolution region than the composite image.

The invention relates to the technical field of remote sensing hyperspectral images, in particular to a remote sensing hyperspectral image super-resolution reconstruction method based on a hybrid neural network, and the method comprises the steps: firstly carrying out the preprocessing of a remote sensing hyperspectral image data set, and obtaining a training set; constructing a hybrid neural network with a spectrum hypergraph branch, a space hypergraph branch, a semantic hypergraph branch, a sensitive wave band extraction branch and a recovery and reconstruction branch; training the hybrid neural network by using a training set to obtain a hybrid network model; and finally, inputting a to-be-reconstructed low-resolution remote sensing hyperspectral image into the hybrid network model to obtain a corresponding reconstructed remote sensing hyperspectral image. According to the method, space, spectrum and semantic feature information are fully combined, and a main body structure and detail change characteristics in an image are captured; the sensitive wave band extraction branch is provided to enhance cross guidance and fusion among multi-domain features, and the effect of super-resolution reconstruction is fully improved.

The invention relates to the technical field of metal defect detection, and discloses an aluminum alloy round aluminum rod surface defect image super-resolution method. The method comprises the following steps: acquiring a low-resolution original image sequence of surface defects of the aluminum alloy round aluminum rod, and synchronously acquiring gray value distribution at different illumination angles through a multi-channel optical sensor; constructing a dynamic degradation model according to pixel displacement of adjacent frames in the original image sequence, extracting cross-scale defect features in the original image sequence, and taking output parameters of the dynamic degradation model as spatial constraint conditions of a feature extraction network; and a high-resolution defect image is generated through the multi-stage residual error reconstruction network, the high-resolution image output by the reconstruction network is fed back to the dynamic degradation model, and the frequency domain response coefficient of the spatial fuzzy kernel function is updated to form closed-loop optimization. The identification degree of defect features is improved, and a reliable image data basis is provided for accurate detection of the surface defects of the aluminum alloy round aluminum rod.