566 results about "Image edge" patented technology

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention provides a rock image classification method based on edge enhancement and multi-scale feature fusion. By combining the edge enhancement and multi-scale feature fusion technology, the accuracy of mineral classification in the rock slice image is remarkably improved. According to the method, rock slice image interference is eliminated through filtering and denoising, and mineral particle edges are enhanced by fusing morphological top-hat transformation and a Laplace operator; and performing multi-scale pyramid decomposition on the enhanced image to extract high-frequency information, performing multi-direction response enhancement to generate a direction feature map, and performing channel-level fusion on the original image, the edge enhanced image and the direction feature map. And finally, inputting the fused image into a convolutional neural network to complete rock type classification. According to the method, mineral boundary expression is effectively enhanced, the classification accuracy is improved, and a reliable technical scheme is provided for geological analysis and lithology identification.

The invention discloses an image edge cutting method and device based on boundary detection, equipment and a storage medium, and relates to the technical field of image boundary detection, and the method comprises the steps: carrying out the preprocessing of an original image, and obtaining a target image; determining a scene type of the target image; based on an edge detection strategy corresponding to the scene type, performing edge detection on the target image to obtain an edge image; performing straight line detection on the edge image to obtain a plurality of candidate straight line segments; and based on a boundary positioning rule, determining a target boundary line segment from the plurality of candidate straight line segments, so as to carry out edge cutting on the original image according to the target boundary line segment. According to the invention, a more reliable image edge cutting effect can be generated.

The invention discloses a lightweight single-image super-resolution reconstruction method based on local and global feature collaborative enhancement perception, which is used for solving the problems of insufficient utilization of high and low frequency clues in single-image super-resolution, low feature fusion efficiency and difficulty in consideration of structural consistency and visual fidelity of reconstructed images. The network adopts a double-flow heterogeneous architecture; a local branch uses multi-type differential convolution explicit coding image edge and texture prior to enhance detail characterization capability; the global branch effectively models long-range dependency and low-frequency semantic information by integrating local, cross-regional and global multi-level spatial self-attention mechanisms. The frequency sensing fusion module provided by the invention generates a channel specific space weight based on frequency characteristics, self-adaptively fuses double-branch characteristics, accurately balances structure maintenance and detail enhancement requirements in a characteristic fusion process, and effectively reduces characteristic redundancy. According to the super-resolution network, the model complexity is remarkably reduced, high-resolution images which are consistent in structure, vivid in vision and rich in details can be generated while lightweight design is kept, and an effective scheme is provided for efficient and high-performance lightweight single-image super-resolution reconstruction model design.

The invention discloses an image edge feature enhancement correction fusion method based on a guide filter, and aims to solve the problems that detail features of a low-illumination visible light image and an infrared image are not obvious, focusing edge information is not clear, and registration of a multi-focus image is wrong. The invention provides an edge feature enhancement correction fusion method based on a guide filter. In the illumination enhancement stage, a visible light image is divided into a base layer and a detail layer through a guide filter, and the image contrast and detail information are enhanced. For an infrared image, an infrared background is reconstructed by using a quadtree decomposition and Bezier interpolation method, unclear focusing edge feature information is extracted, and the visibility of image details is enhanced. And finally, reconstructing the two processed images by using a multi-scale weighted gradient method, and performing fusion by calculating large-scale and small-scale weight scales.

The invention belongs to the field of photovoltaic panel hot plate image processing, and particularly relates to a photovoltaic panel defect detection method fusing multi-scale wavelets and a lightweight attention mechanism. According to the method, an RHDWT discrete wavelet transform module based on multi-scale decomposition is added in a model input stage to strengthen image edge and texture detail representation; a Mix Structure Block module is introduced into a backbone network of the YOLOv11, so that multi-scale features are fused, and the feature expression capability is improved; an LWGA lightweight global attention mechanism is introduced into a neural network connection layer to enhance the context modeling capability, and the detection effect on small target defects such as fine cracks and hot spots is improved. According to the model, through collaborative optimization in three aspects of input preprocessing, feature extraction and an attention mechanism, the precision and robustness of defect detection in a complex photovoltaic module infrared or visible light image are remarkably improved, and the model is suitable for scenes such as high-precision photovoltaic panel image detection and intelligent maintenance.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention provides a scanning electron microscope image edge detection method, which comprises the following steps: carrying out anisotropic diffusion filtering on a scanning electron microscope image to suppress noise and reserve edges to obtain a filtered image; an original scale gradient and a down-sampling scale gradient of the filtered image are calculated, a plurality of pixel regions in the original scale gradient and the down-sampling scale gradient are fused based on a plurality of adaptive weights to obtain a gradient map, the adaptive weights are determined based on the complexity of the texture of the filtered image, and each pixel region corresponds to one adaptive weight; multi-dimensional features are extracted from the gradient map, the multi-dimensional features are input into a machine learning model for threshold prediction, a continuous pixel-level threshold map is generated based on a predicted threshold, and a gradient threshold included in the continuous pixel-level threshold map is a critical value for distinguishing different types of pixels in the gradient map; and comparing the gradient map with the continuous pixel-level threshold map to obtain a target edge pixel, and generating an edge detection result map based on the target edge pixel.

The invention provides a remote sensing image cloud removal method and system fusing gradient fidelity and time-spectrum consistency, and relates to the technical field of remote sensing image processing and tensor modeling, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image and cloud mask data, and generating a fault mask through active fault recognition; then calculating a guide gradient tensor and carrying out low-rank approximate processing to obtain a spatial characteristic factor and a time-spectrum characteristic factor; constructing a multi-objective optimization model of a gradient domain fidelity term, a pixel domain fidelity term and a time-spectrum consistency constraint term based on the multi-objective optimization model; and a near-end alternating minimization algorithm of an embedded alternating direction multiplier method is adopted to efficiently solve, a reconstruction result is corrected in combination with a cloud mask and an active fault mask, and a high-quality cloud-removed image sequence is output. According to the method, the edge structure, texture details and space-time consistency of the image can be effectively kept in a complex cloud coverage scene, and the usability and analysis value of remote sensing data are remarkably improved.

The invention relates to the technical field of contour extraction, in particular to a knitted label edge contour extraction and optimization method, which comprises the following steps of: extracting an image edge gray scale trend and acquiring a fracture coordinate, extracting an edge track to generate a connection path, reconstructing an interference region connection path, converting rough edge region path distribution and screening out an abnormal path. According to the method, the gray abrupt change and edge interruption features in the image are identified, the fracture area is accurately positioned, structural integrity identification is enhanced, space correlation and direction fitting are carried out on edge endpoints in the interference area, the boundary recovery capability and the closure degree are improved, abnormal path segments are screened out through polar distribution, and therefore the detection accuracy is improved. Control nodes are set based on the position relation between the boundary segments and the overall chain, a boundary joint trend coordination structure is fused, the contour consistency and robustness under the complex background are improved, and the coherence recognition capability and extraction precision are enhanced.

The invention relates to the technical field of medical image processing, and discloses an ultrasonic image edge segmentation method and system, a terminal and a medium, and the ultrasonic image edge segmentation method comprises the steps: constructing an initial model, obtaining a training set, inputting the training set into the initial model, and obtaining a prediction probability graph; performing data preprocessing on the training set to obtain boundary supervision information; boundary sensing information is obtained according to the prediction probability graph and the boundary supervision information; obtaining a target model according to the boundary perception information, the training set and the initial model; and acquiring an echocardiogram of a user, and inputting the echocardiogram into the target model to obtain a segmentation probability graph. According to the method, the boundary monitoring information is provided by preprocessing the training set to solve the problem of boundary blur, the perceptual ability of the model to the boundary is enhanced through the boundary monitoring information, and the boundary segmentation precision of the ultrasonic image can be improved.

The invention provides a gas-liquid interface identification and ship sloshing liquid level correction method and system, and the method comprises the steps: extracting image edge and gray texture features, achieving the state identification of a liquid-gas interface through a lightweight convolutional neural network model, and dynamically generating a collection strategy according to the state identification; then collecting original liquid level signals and synchronously obtaining IMU data, extracting a multi-mode component through signal decomposition, constructing a disturbance recognition model by combining an IMU, filtering disturbance related components and reconstructing a stable liquid level; and further modeling the disturbance component into a residual sequence, realizing disturbance trend prediction and liquid level dynamic correction by using a time sequence prediction model, and outputting more stable and reliable liquid level data. The problem that liquid level measurement is unstable in the LNG ship sloshing environment can be effectively solved, image recognition and IMU data are fused, liquid level disturbance suppression and dynamic correction are achieved, and the method has the advantages of being self-adaptive, resistant to interference, stable in measurement and the like and is suitable for high-precision liquid level monitoring under the complex working condition.

The invention relates to the field of digital image processing, in particular to a super-resolution imaging method based on focal plane splicing and adaptive fusion. According to the method, sub-pixel offset among nine CCDs is preset through hardware, and nine frames of low-resolution image sequences with accurate displacement are obtained in push-broom. A central image is taken as a reference frame, high-precision mapping is realized based on hardware offset, motion estimation errors are avoided, effective pixels are screened by calculating robustness weight, an anisotropic Gaussian kernel function with a self-adaptive local structure is constructed so as to maintain image edge and detail features, and each frame is accumulated to a high-resolution grid in a weighting mode, so that a high-resolution image is obtained. And a sample compensation mechanism based on cumulative robustness is introduced, a fusion strategy is adaptively adjusted in an information insufficient area, and finally a high-resolution image is generated through normalization. The method significantly improves the imaging quality, suppresses artifacts and noise, and is suitable for the field of satellite remote sensing.

The invention relates to the technical field of ocean detection, in particular to a DBN-based underwater target tracking system, which comprises a multi-source data processing module, an environment monitoring module, a feature fusion module, a target feature extraction module and a dynamic prediction recognition module. According to the invention, through real-time signal-to-noise ratio evaluation of sonar and visual data and dynamic adjustment of modal weight, data reliability under the condition of insufficient illumination or complex water quality is ensured, and a data fusion strategy is optimized in combination with illumination intensity, turbid concentration and visible distance, so that the system has good environmental adaptability. Time sequence features and image edge gradients are utilized, key geometric differences are extracted, the feature recognition distinction degree is improved, significant edge features are screened and optimized, a target area is highlighted, the recognition precision is effectively improved, and target tracking conditions can be judged in real time based on migration analysis of DBN node response; and the underwater detection efficiency and the applicability in a complex environment are improved.

The invention relates to the technical field of automatic driving, in particular to a multi-modal three-dimensional target detection method, which comprises the following steps of: in a first stage, firstly acquiring neighbor depth information of point cloud to enhance image features, and then further enhancing image edges and reducing semantic confusion by utilizing wavelet transform; and then a cross attention mechanism is introduced to realize effective fusion of the enhanced image features and the point cloud, an initial region suggestion is obtained, and bounding box classification and prediction in the first stage are realized. And in the second stage, designing a double-attention module based on grid features to supplement more geometric detail information, acquiring more context information and position information by utilizing the initially suggested spatial features and channel features generated in the first stage, and then introducing a self-attention mechanism to dynamically distribute interaction weights of the grid features, so as to realize the self-attention interaction of the grid features. Rich context information is effectively captured, a local geometric structure is better coded, information loss is reduced, and the detection precision of the model is improved.

The invention discloses a low-light image enhancement method and system based on edge extraction and feature fusion, and aims to improve the image quality under a low-light condition, and the method comprises the steps: differential convolution edge extraction, feature fusion and image enhancement, effective extraction of image edge information through a differential convolution kernel, and combination of global and local features through a feature fusion module. The image enhancement module utilizes a deep learning network to improve image brightness and suppress noise, the method is suitable for embedded equipment with a high-performance computing environment and limited resources, the performance is excellent on a plurality of data sets through experimental verification, the image definition and details are remarkably improved, and the method is suitable for the fields of security monitoring, automatic driving and the like and has a wide application prospect.

The invention relates to a precision agriculture monitoring system and method based on multispectral imaging. The system and method are applied to real-time monitoring of crop physiological parameters and variable fertilization decision making. The system comprises an unmanned aerial vehicle imaging module, an edge computing unit and a cloud analysis server. The unmanned aerial vehicle module is provided with a multispectral filter wheel, a three-axis holder and an RTK positioning device and is used for acquiring a high-resolution crop image; the edge calculation unit integrates a radiation correction module, an image splicing module and a canopy segmentation module to realize on-site preprocessing; the cloud server runs a deep learning model and a feature fusion mechanism, outputs estimation of parameters such as nitrogen, chlorophyll and moisture, and generates a high-resolution fertilization prescription map. In the aspect of the method, dynamic monitoring of the nitrogen content of crops is realized through route planning, data synchronization, radiation normalization, multi-source feature fusion and time sequence prediction. The system supports online updating and ground verification of the model, has high precision, low delay and large-area operation capability, and is suitable for intelligent agriculture and precise fertilization scenes.

The invention discloses a vision enhancement robust digital dark watermarking method based on deep learning, a storage medium and equipment, and belongs to the technical field of digital image processing and information security. According to the method, a visual enhancement module comprising a self-adaptive watermark region adjustment module and a frequency enhancement module is constructed, and a multi-region local loss training mechanism and a screen shooting simulation module are combined, so that the visual quality of a watermark image is improved, and meanwhile, the robustness of the watermark image for resisting physical attacks such as screen shooting is enhanced. The specific processing flow comprises the following steps: acquiring an original image and watermark information; embedding the watermark information into the image by using an encoder, wherein an embedded region is optimized by a self-adaptive region guide matrix generated based on the image edge and gray information; in the model training process, a noise layer simulating screen shooting physical distortion is introduced, and the area weight is dynamically adjusted according to residual error distribution in the later stage of training so as to focus and optimize the image quality; watermark information is extracted from an image which may suffer from an attack by using a decoder.

The invention discloses an image processing method and device, a medium and a computer program product, and relates to the technical field of semiconductor manufacturing. The image processing method comprises the following steps: acquiring target image samples of a plurality of wafer samples through a scanning electron microscope, and obtaining a first contour image label of each target image sample; based on the target image sample, a first contour image result is generated through a generator of a preset image edge model, iteration training is carried out on the preset image edge model through pixel difference loss, adversarial loss and edge supervision loss until a convergence condition is met, and a target image edge model is obtained; the generator of the target image edge model can output an edge contour image based on the target image of the target wafer. Through multi-dimensional constraints of pixel difference loss, edge supervision loss and adversarial loss, the preset image edge model is trained in the direction of generating an actual edge contour, and the accuracy of the edge contour image is improved.

The invention discloses an SPR (Surface Plasmon Resonance) image optimization processing method based on image segmentation and edge enhancement, which comprises the following steps: acquiring SPR image data, and preprocessing to generate standardized SPR image data; inputting a structure boundary extraction model constructed based on CGNet, generating a structure boundary label graph, and aligning the structure boundary label graph with the image; gradient amplitude and local entropy mutation detection is executed, and an artifact guide graph is generated; respectively inputting the standardized image into details and context branches of the improved CSDNet, and extracting edge and semantic feature maps; inputting a guide perception gating module, executing structure enhancement and artifact suppression fusion, and generating a fusion feature map; inputting into a multi-scale detail recovery module, and outputting an edge enhanced image; and executing structural similarity and marginal definition scoring based on the original image and the enhanced image, and generating an optimization result. According to the method, synchronous optimization of SPR image edge enhancement and structure maintenance is realized, and the image definition and diagnosis availability are remarkably improved.

The invention belongs to the technical field of optical imaging, and discloses a multifunctional image edge enhancement device based on spiral phase contrast imaging, which comprises a light source system, an optical imaging system and an edge enhancement detection system which are arranged in sequence, the light source system is used for generating collimated linear polarization laser; the optical imaging system comprises a first 4-f system and a round hole diaphragm which are sequentially arranged on a light path; a sample to be tested is arranged between the light source system and the first 4-f system; the edge enhancement detection system comprises a first lens, a vortex wave plate, a second lens and an imaging unit which are sequentially arranged on a light path; the first lens and the second lens form a second 4-f system; linearly polarized laser output by the light source system is transmitted by a to-be-detected sample, enters the first lens after passing through the first 4-f system and the round hole diaphragm, and then is imaged on the imaging unit after passing through the vortex wave plate and the second lens in sequence. According to the invention, edge enhancement can be realized, and the contrast and resolution of the image are improved.

The invention relates to the technical field of image processing, and provides an underwater image enhancement method based on frequency domain enhancement and edge guidance, which comprises the following steps of: firstly, acquiring three images with different scales through image downsampling, and then converting the three images into a frequency domain to respectively extract a low-frequency spectrum and a high-frequency spectrum; constructing a distance mask and a mean value mask based on the distance and the amplitude mean value to enhance a high-frequency part so as to improve image details; meanwhile, the effect is enhanced by suppressing low-frequency part noise. In addition, a multi-stage residual feature aggregation module is provided, the module focuses on detail extraction, and information loss caused by global enhancement is effectively avoided. And finally, further enhancing image edge details in combination with an edge guiding strategy. Experimental results show that the method is superior to the current most advanced underwater image enhancement method in quantitative and qualitative evaluation of a plurality of public data sets.

The invention relates to the technical field of optical imaging, and discloses an optical lens splicing imaging method and system, and the system comprises a calibration module, a preprocessing module, a registration module, a correction module, and a fusion module. Through the arrangement of the registration module, the correction module and the fusion module, the problem of registration errors caused by sparse feature points of a low-texture region can be solved through a mixed strategy of global mutual information guidance and local robust matching, the registration precision is effectively improved, the optical aberration of each sub-lens is compensated through a global model based on a Zernike polynomial, and the registration precision is improved. The image quality of the marginal area of the spliced image is effectively improved, a multi-resolution pyramid optimization mode is adopted, the calculation complexity of global registration is reduced, the real-time imaging requirement is met, meanwhile, splicing seams are effectively eliminated, and the overall smoothness of the image is remarkably improved.

The invention relates to the technical field of imaging medicine processing, and discloses an imaging medicine multi-modal data fusion processing system. The system comprises a multi-modal data acquisition module, a dynamic PET parameterization analysis module, a dose-image quality optimization module, a multi-target fusion model and a hierarchical processing control module. The multi-modal data acquisition module is used for acquiring a dynamic PET time-activity curve, and the dynamic PET parameterization analysis module is used for modeling to generate a dynamic parameterization image; the dose-image quality optimization module constructs a correlation model and outputs an optimal dose distribution scheme; performing feature level fusion on the multi-target fusion model to generate a multi-modal fusion image; and the hierarchical processing control module realizes focus region segmentation, image edge enhancement and image reconstruction parameter adjustment through a global optimization layer, a local correction layer and an execution layer. The system effectively fuses the multi-modal image information, optimizes the image quality, and improves the accuracy and efficiency of disease diagnosis.

The invention discloses a light-weight YOLO-based cotton disease and insect pest real-time identification system and method, and the method can effectively extract multi-scale features through the combination of an MSFPN multi-scale feature pyramid backbone network and depth separable convolution and multi-branch residual connection, and enables a model to have a good recognition capability for diseases and insect pests of different sizes. According to the adaptive deformable convolution, sampling points are dynamically adjusted according to spatial distribution of a disease and insect pest area, image features are subjected to weighted fusion through cross-modal attention gating, and capture of complex disease and insect pest features is enhanced. By means of strategies such as multi-round iteration and dynamic threshold adjustment of the NMS non-maximum suppression optimization algorithm, repeated annotation and missing detection and false alarm are effectively reduced, particularly, the recognition precision of dense small targets and large-area contiguous scabs is obviously improved, the small target detection rate is increased, the image edge pest and disease damage detection rate is increased, and the overall detection accuracy is guaranteed.

The invention discloses a microscopic image integer pixel alignment method based on sub-pixel edge offset detection, and relates to the field of microscopic image processing. The problems that an existing sub-pixel registration technology depends on iterative optimization or a complex deformation model, and real-time processing is difficult; sensor sampling errors and mechanical translation errors need to be compensated through a high-resolution camera or a precise displacement table, and the system cost is high. According to the invention, through sub-pixel-level offset compensation, sub-pixel alignment can be realized without depending on a high-precision displacement table, and the hardware cost is remarkably reduced. The method comprises the following steps: extracting image edge feature points by using an edge detection algorithm, fitting a gray centroid to calculate a sub-pixel offset, and counting an average offset; generating an affine transformation matrix according to the average offset, resampling the image, and aligning pixel values to an integer coordinate grid; and finally, cutting the image edge according to the interpolation algorithm type to eliminate the ghost. The method is also suitable for the application fields of fluorescence microscope multi-view splicing, super-resolution reconstruction and the like.

The invention provides a vehicle management method based on visual identification. Comprising the following steps: performing visual image acquisition and basic feature identification on a vehicle in a target area, executing multi-source interference perception, acquiring raindrop volume concentration, a strong light incidence angle, vehicle tail gas heat flux density and a light intensity sudden change rate of the target area, and generating multi-factor coupling interference intensity data; executing dynamic exposure control based on the multi-factor coupling interference intensity data, outputting an exposure compensation coefficient and driving an image acquisition unit to adjust the shutter speed and the exposure duration; performing distortion correction feature extraction based on the image after exposure compensation coefficient correction, calculating image marginal definition and feature matching frequency deviation, and generating feature extraction confidence; judging whether to output a vehicle management recognition result or not according to the feature extraction confidence; the method can effectively deal with the composite interference scene, and solves the problems of low recognition precision and response lag.

The invention relates to the technical field of image processing, and provides a pipeline robot pipeline defect identification method and device for pipeline safety overhaul, and the method comprises the steps: obtaining continuous frames of pipeline inner wall images through the driving of a pipeline robot in a pipeline; extracting a plurality of feature points from each frame of pipeline inner wall image to obtain a plurality of matched feature point pairs in two adjacent frames of pipeline inner wall images; based on the distribution of the edges of the two adjacent frames of pipeline inner wall images and other edges, obtaining the crack possibility of each edge in each pipeline inner wall image; the crack possibility difference of the same edge in the pipeline inner wall images of the adjacent scales is analyzed, and the interval regularity of all the edges is obtained; obtaining the gray uniformity of each edge; the crack authenticity of each edge is obtained; and acquiring the crack density of each pipeline inner wall image, and carrying out pipeline defect identification and alarm based on the crack density. The invention aims to solve the problem of recognition errors caused by the fact that internal defects of a pipeline are similar to texture features.

The embodiment of the invention provides a CEST image super-resolution reconstruction method, medium and equipment, and the method comprises the steps: obtaining an original image and a reference image of a target object, the original image being a molecular function image collected through a chemical exchange saturation transfer imaging technology, and the reference image being an anatomical structure image collected through a magnetic resonance imaging technology; through a high-frequency, intermediate-frequency and low-frequency fusion enhancement network, extraction and fusion enhancement of corresponding high-frequency, intermediate-frequency and low-frequency characteristics are carried out on an original image and a reference image. According to the method, anatomical edge details of a reference image are captured through a high-frequency fusion enhancement network to enhance image edge details, image texture features are captured through an intermediate-frequency fusion enhancement network to enhance image texture details, image background details are enhanced through a low-frequency fusion enhancement network, and then images with enhanced frequency band information are fused. A reconstructed image with high resolution and molecular function signals is obtained, and the reconstruction precision of super-resolution reconstruction is improved.

The invention relates to a geological fracture recognition method based on multi-source geophysical data and a computer system, and solves the problem of high-precision recognition of tiny fractures and complex fracture zones. The method comprises the following steps: acquiring gravitational field model data, full-tensor gravity gradient data and aeromagnetic data of a target area, and carrying out gridding processing, pole conversion processing and edge expansion preprocessing on the data; performing boundary identification calculation on the preprocessed data by adopting a traditional physical field multi-derivative algorithm to generate a first group of boundary information A; carrying out boundary recognition calculation on the preprocessed data by adopting a gravity and full-tensor gravity gradient combined derivative algorithm (an enhanced total horizontal gradient derivative algorithm, a combined derivative algorithm and a combined oblique derivative algorithm) to generate a second group of boundary information B, and carrying out boundary recognition calculation on the preprocessed data by adopting an image edge detection algorithm to generate a second group of boundary information B; generating a third group of boundary information C; and carrying out weighted fusion on the three groups of boundary information to obtain a final fracture identification result.