448 results about "Deblurring" patented technology

The invention relates to the technical field of fabric detection, and discloses a fabric defect intelligent detection method and system based on AI visual identification. According to the method, motion blur is quantized through motion state data, optical blur caused by fabric motion is eliminated through deconvolution solution, so that motion interference in the fabric transmission process is processed in a targeted mode, self-adaptive balance of the deblurring capacity and the feature retention capacity is achieved, and then based on the optical interference principle, the deblurring capacity and the feature retention capacity are improved. Through a dynamic calibration system combining hardware-level real-time compensation and multi-dimensional optical parameter calibration, dynamic optical parameter calibration of primary correction data is realized, then fabric defect characterization data is extracted to accurately obtain defect features, and finally, a detection-production line control closed loop is constructed through a quality quantitative index and a comprehensive risk value, so that fabric defect detection is realized. The fabric defect detection precision can be improved, so that the problem of high defect missing detection and false detection rate caused by optical data distortion due to movement and environment interference in a traditional method is effectively solved.

The invention discloses an event information guided image deblurring and high-frame-rate reconstruction method. The method comprises the following steps: step 1, obtaining a blurred image and an event stream corresponding to the blurred image to construct a training data set; 2, constructing a deblurring and frame insertion combined reconstruction network based on a dynamic cross-modal fusion module; step 3, training to obtain a trained joint reconstruction network based on the cross-modal attention mechanism and the UNet architecture; and step 4, inputting a to-be-reconstructed blurred image and a corresponding event stream into the trained deblurring and frame insertion combined reconstruction network based on the dynamic cross-modal fusion module to obtain a final multi-frame reconstruction image. According to the method, a higher weight dynamic state is given to an event mode in a high-speed motion scene, an image data weight is added in a static scene, and a scale feature fusion strategy is adopted for a seriously fuzzy scene, so that the problems of semantic difference and noise interference are reduced, and the image recovery quality of a fuzzy region is improved.

The invention discloses an image deblurring method based on a rotation perception multi-dimensional attention and blurring sensitivity self-adaptive distribution mechanism, aiming at the defects of the prior art in the aspects of complex blurring processing and image reality sense improvement, and belongs to the technical field of image processing, and the method comprises the following steps: preprocessing a collected picture to obtain a training set; training an image deblurring model by using the training set; and performing image deblurring by using the trained image deblurring model. According to the invention, by designing a plurality of innovative modules and combining fuzzy degree adaptive selection, kernel estimation and dynamic weight distribution driven by a physical model, a multi-dimensional rotation perception attention mechanism, spectrum reconstruction anti-noise deblurring and detail enhancement of perception loss optimization, the image deblurring effect and processing efficiency are effectively improved; the method is especially suitable for processing blurred images with high complexity and high resolution.

The invention relates to a high-precision three-dimensional Gaussian reconstruction method based on fuzzy perception and joint optimization, and belongs to the field of computer vision and graphic images. The method comprises the following steps: firstly, constructing a fuzzy sensing module, accurately identifying a fuzzy type and an area of an input multi-view fuzzy image, and generating a corresponding fuzzy mask; performing three-dimensional Gaussian representation based on the input multi-view blurred image, estimating internal and external parameters of a camera and sparse point cloud data of a scene, and obtaining initial three-dimensional Gaussian scene representation; and then performing joint optimization on a Gaussian ellipsoid in the initial three-dimensional Gaussian scene representation in combination with an input multi-view blurred image and a blurred mask to generate a clear three-dimensional scene representation. And finally, performing three-dimensional Gaussian reconstruction rendering by using the optimized three-dimensional Gaussian scene representation, and outputting a high-precision three-dimensional Gaussian reconstruction result and a clear rendered image. The method can effectively improve the three-dimensional reconstruction precision and rendering quality under the fuzzy input condition, has high adaptability and stability, and can be applied to the fields of image deblurring, virtual reality and the like.

The invention provides a motion blurred bar code identification method and system based on multi-frame image fusion, and the method comprises the steps: obtaining a continuous image sequence containing a motion blurred bar code, carrying out the motion track feature extraction of the continuous image sequence, and obtaining a motion vector field and a pixel displacement track set of a bar code region in each frame image unit; performing multi-frame image fusion on the continuous image sequence based on the motion vector field and the pixel displacement track set to generate a candidate bar code image set; performing deblurring enhancement processing on the candidate bar code image set to obtain a clear bar code image unit after deblurring processing; and performing bar code area positioning and distortion correction processing on the clear bar code image unit to generate a standardized bar code image, and performing identification to obtain an identification result. According to the invention, the accuracy and reliability of bar code identification in a motion blurred scene can be obviously improved.

The invention provides an inspection vehicle cooperative positioning method and system based on a dynamic fuzzy image, and relates to the technical field of tunnel inspection, and the method comprises the following steps: S1, carrying out slam modeling through a laser radar; s2, the tethered unmanned aerial vehicle performs target detection on the AprilTags codes arranged in the tunnel at equal intervals through a carried RGB camera, and if a first AprilTags code candidate area is obtained through detection, the step S3 is executed; s3, performing deblurring processing on the first AprilTags code candidate region, performing image definition judgment on the processed first AprilTags code candidate region, and if the image is judged to be unclear, turning to step S4; if the image is judged to be clear, turning to step S5; s4, a control instruction is sent to the free unmanned aerial vehicle according to the first AprilTags code candidate area, a second AprilTags code candidate area is obtained, and the step S5 is executed; and S5, positioning according to the processed first AprilTags code candidate area or the processed second AprilTags code candidate area to obtain the position information of the inspection vehicle.

The invention discloses a robot vision external parameter calibration method based on blind deblurring and natural vibration measurement, and the method comprises the steps: collecting a continuous image sequence in a vibration environment through a high-frame-rate camera, extracting pixel-level motion information in combination with an advanced image deblurring deep network and an optical flow feature tracking technology, and carrying out the self-vibration measurement. And further restoring a three-dimensional displacement signal caused by natural vibration of the camera based on a pinhole imaging model, and obtaining ideal position information through frequency domain analysis to realize high-precision visual system external parameter calibration under a vibration condition.

The invention discloses a mold defect visual inspection method, particularly relates to the technical field of industrial machine visual inspection, and is used for solving the technical problem of image spatial variation blurring caused by mechanical vibration under a mobile shooting condition. The method comprises the following steps: acquiring a to-be-detected image on the surface of a mold, analyzing the gradient magnitude of each region, determining the fuzzy characteristics of different regions in the to-be-detected image according to the difference of the gradient magnitudes, evaluating the expected confidence of each region for executing the deblurring operation based on the fuzzy characteristics, and executing the deblurring operation on the regions to obtain a preliminary restored image; an artifact index is calculated in the uniform background area of the preliminary restored image, the distribution concentration degree of image components in each local feature area in the frequency domain is analyzed in the preliminary restored image, and the distribution concentration degree is compared with a preset defect judgment threshold value adjusted according to the artifact index; judging whether the corresponding local feature region is a defect region or not; accurate recognition of mold surface defects under complex imaging conditions is realized.

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention discloses a 5G signal tower three-dimensional rendering and high-altitude panoramic image synthesis method based on GSII, and the method comprises the steps: firstly carrying out the frame extraction processing of data shot when an unmanned plane flies around a signal tower, and obtaining an original image set; obtaining a signal tower sparse point cloud and a camera pose through a motion structure recovery SfM algorithm, and carrying out 3DGS initialization operation; the method is characterized in that in the process of iterative optimization of a three-dimensional Gaussian point cloud, a GSII network is constructed, structural similarity comparison is carried out on an original image of the same visual angle and a deblurred rendering image, a loss function is sensed by using a high receptive field and back propagation is carried out, meanwhile, parameter updating is carried out on a 3D Gaussian ball attribute and FFC residual error repair network, and the quality of a new visual angle rendering image is improved; and finally, designing a central pixel time sequence splicing module, and converting any number of single-view-angle rendering images into a panoramic image. According to the method, the phenomena of artifacts and blurring which are easy to occur under a high-altitude view angle are solved, and the signal tower high-altitude panorama is finally obtained by synthesizing a plurality of rendering images of a single view angle.

The invention provides a panchromatic sharpening joint optimization method and system based on three-stage progressive fusion. The method comprises three stages of coarse fusion, deblurring detail enhancement and fine fusion. The method comprises the following steps: firstly, performing feature extraction and preliminary fusion on a low-resolution multispectral image and a high-resolution panchromatic image through a dual-path mutual enhancement network; secondly, a multi-layer stacked deblurring module is introduced to perform deep enhancement on fusion features, and the details and definition of the image are effectively improved in combination with partial large kernel convolution, channel mixing and an element-level attention mechanism; and finally, realizing fine optimization of spectrum consistency and a space structure, and outputting a fused image with high spatial resolution and high spectral fidelity. According to the method, the stability and reconstruction quality of panchromatic sharpening under a complex degradation condition are effectively improved through multi-source remote sensing image collaborative modeling and progressive feature fusion, and the method has good practical value and popularization prospect and is superior to a current panchromatic sharpening method based on deep learning.

The invention relates to the related technical field of computer vision, in particular to an event-guided image motion deblurring method in a real scene, which comprises the following steps of: 1, constructing an event-image two-branch fusion deblurring network, and obtaining effective information in a blurred image and an event stream to realize motion blurred image restoration; and 2, in the event branch, converting a spatially sparse event stream into a dense three-dimensional tensor form by using a voxel grid event representation method, and obtaining a spatio-temporal motion feature representation of the event through a spatio-temporal motion enhancement module. According to the event-guided image motion deblurring method in the real scene, an event stream in exposure time is divided into N time slices, the N time slices are respectively accumulated into 2xHxW event frames according to polarities, a 2NxHxW three-dimensional event tensor is constructed, time sequence dependency in 2N event channels is modeled by adopting a bidirectional channel scanning strategy based on a selection state space model, and the time sequence dependency in the 2N event channels is calculated. Information of motion over time over the exposure time is modeled.

The invention relates to the technical field of image processing, and discloses an image deblurring model and method based on double-domain feature fusion, and the method comprises the following steps: collecting data, and building a blurred image data set; building a DAF-UNet neural network based on spatial domain and frequency domain feature fusion; the DAF-UNet neural network is composed of n UNet sub-networks with different scales, and n is a positive integer; based on the blurred image data set, training the DAF-UNet neural network to obtain a trained image deblurring model; collecting a to-be-processed image; and processing a to-be-processed image by using the trained image deblurring model to obtain a clear image result. According to the method, the problems of low information mobility and key feature loss in the prior art are solved, and the method has the characteristics of good image recovery effect and model miniaturization.

The invention discloses a joint injury identification method and system based on arthroscopic imaging. The method comprises the steps that an original image, collected by an arthroscope, of intra-articular tissue is acquired; sequentially performing denoising, artifact removal and deblurring processing on the original image to obtain a preliminary original image; performing corresponding semantic segmentation according to the initial original image so as to segment sub-regions corresponding to different tissues on the initial original image, and performing image enhancement on the different sub-regions by adopting corresponding image enhancement strategies to obtain a target image; and performing visual display on the target image through a display device so as to perform joint injury identification on the current joint. According to the method, arthroscope imaging is enhanced, and medical staff are assisted in recognizing the joint injury state.

The invention discloses a video deblurring method based on a deformable space-time sparse converter. The method comprises the following steps: carrying out feature extraction on continuous n frames of fuzzy videos, and calculating forward and backward light streams and corresponding fuzzy images; iteratively updating features of each frame through a bidirectional feature propagation module guided by a multi-scale fuzzy image, and aggregating feature information of different propagation branches; a deformable space-time sparse Transform module is adopted to carry out refinement processing on the aggregation features; and inputting the refined features into a decoder module, and generating a final deblurring result in combination with original input residual connection. The method is suitable for scenes of security monitoring, mobile equipment shooting and the like, the effect of removing the dynamic fuzzy area in the video can be improved, and the overall visual quality of the video is effectively improved.

The embodiment of the invention provides an image processing method, a computer program product, equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed image, wherein the to-be-processed image is a blurred image with an unknown blurred domain; converting the Y-channel image of the to-be-processed image into a Y-channel image with a known fuzzy domain by using a pre-trained generator of a generative adversarial model, and performing deblurring processing on the Y-channel image with the known fuzzy domain to obtain a clear Y-channel image; and performing enhancement processing on the edge region of the clear Y channel image, and fusing the image obtained by the enhancement processing with the UV channel image of the to-be-processed image to obtain a clear image corresponding to the to-be-processed image. Through the method provided by the invention, a clear image with a clear edge and a good effect can be obtained, and the effect of the clear image obtained through deblurring processing is ensured.

The invention relates to an automatic milling cutter setting method and system based on machine vision, and belongs to the technical field of milling cutter setting. The method comprises the following steps: firstly, positioning initial position coordinates of a milling cutter, and planning an initial tool setting path of the milling cutter by combining target tool setting position coordinates; then obtaining an image of the milling cutter in the initial cutter setting path, carrying out image denoising and image deblurring processing, carrying out edge detection after obtaining a second image, extracting an edge contour of the milling cutter, calculating sub-pixel coordinates of edge points of the contour, and carrying out parametric fitting to obtain a current milling cutter position and a current milling cutter posture; inputting the initial tool setting path, the wear degree of the milling cutter, the current position of the milling cutter and the posture of the milling cutter into an error prediction model to predict the current motion error of the milling cutter; calculating the path compensation amount according to the current motion error of the milling cutter, and adjusting the tool setting path of the milling cutter according to the compensation amount. The method can reduce the interference of the motion blur of the milling cutter and environmental factors, and realizes the quantitative adjustment and correction of the tool setting of the milling cutter.

The invention relates to the field of image deblurring, and discloses a multi-scale image deblurring method based on a potential space condition diffusion model, comprising the following steps: constructing a multi-scale image deblurring network which comprises a condition diffusion model and a sliding window attention module, the conditional diffusion model is used for generating a multi-scale prior feature from the fuzzy condition vector in a potential space; the sliding window attention module is a U-shaped network based on an encoder-decoder and is used for executing image deblurring feature regression guided by multi-scale prior features; training the network by adopting a two-stage strategy comprising pre-training and post-training; and inputting a blurred image to be processed into the trained multi-scale image deblurring network, and outputting a final deblurred image. According to the method disclosed by the invention, the common problems of excessive smoothness and artifacts in the deblurring process can be effectively inhibited while the calculation efficiency is ensured, and the recovery precision of texture details and edge structures is improved.

According to the road surface pothole and small roadblock detection method based on YOLOv8 provided by the invention, an ultra-small target detection model is constructed based on a YOLOv8 model, in the ultra-small target detection model, a fuzzy adaptation C3 module is used to replace a C2f module in a backbone network of the YOLOv8 model, deblurring processing is carried out on an input image based on a fuzzy correction module, then image features are extracted, and a detection result is obtained. And through CBAM (Convolution Attention Maintenance) processing, the detection precision can be kept in a fuzzy scene.

The invention discloses a three-dimensional scene reconstruction deblurring method, system, equipment and medium, and the method comprises the steps: firstly, processing a blurred image through employing a dense unconstrained three-dimensional reconstruction frame, generating an initial point cloud and a corresponding confidence score, then employing a confidence balance sampling strategy, sampling a preset number of points from the initial point cloud, and carrying out the sampling of a preset number of points; and finally, taking the high-quality sampling point cloud as initial input of a three-dimensional Gaussian primitive, performing alignment optimization on a potential sharp image and a camera track after the blurred image is decoupled, and outputting a deblurred three-dimensional scene model. By adopting the method, high-quality blurred image deblurring and new view angle synthesis in a low-illumination and dynamic scene can be realized, and the geometric accuracy and semantic definition of a reconstruction result are improved.

The invention discloses a low-light image enhancement method based on multilevel feature fusion. The low-light image enhancement method comprises the steps of acquiring a data set, dividing the data set, extracting features, constructing a synchronous multi-scale network, training the synchronous multi-scale network and testing the synchronous multi-scale network. According to the synchronous multi-scale low-light image enhancement method in combination with the Laplacian pyramid, the input image is processed in parallel by adopting a double-path structure: the preliminary enhancement image is obtained through the local-global convolutional neural network, and the detail and texture information of the image is enhanced based on the Laplacian pyramid decomposition network. A multi-scale network is adopted to process images in scenes of deblurring, defogging, rain removal, low light enhancement and the like, and details and features are extracted in a layered manner, so that the definition, color and contrast ratio of the images are effectively improved. Comparison experiments prove that the method has the advantages that noise is effectively suppressed, and remarkable effects are achieved in the aspects of detail recovery and color restoration. The method is suitable for image enhancement processing under various complex illumination conditions.

The invention relates to a space-frequency domain collaborative low-light deblurring method based on Fourier transform and Mama architecture, and belongs to the technical field of image processing. According to the method, an image is decomposed through Fourier transform, and effective separation of low light degradation and fuzzy degradation is achieved at a frequency domain source according to the physical characteristics of amplitude spectrum dominant illumination and a phase spectrum dominant structure. In order to solve the problem of accurate reconstruction of frequency domain information in a spatial domain, a space-frequency domain double-branch collaborative architecture is constructed, global illumination recovery and local detail enhancement are respectively focused through low-frequency and high-frequency processing branches which are explicitly distinguished in the spatial domain, and frequency domain decoupling advantages and spatial domain sensing capability are adaptively bridged by combining a dynamic feature fusion mechanism. Meanwhile, in order to process different degradation types in the recovery process, a task-oriented decoupling optimization strategy is designed, the strategy activates amplitude adjustment and low-frequency optimization in the encoding stage to deal with low-light enhancement preferentially, and activates phase reconstruction and high-frequency enhancement in the decoding stage to process blurring removal in a targeted mode.

The invention provides a fan blade defect detection method and device based on dynamic inspection of an unmanned aerial vehicle, and relates to the technical field of fan detection. The method comprises the following steps: based on a blade motion parameter, a flight state parameter of an unmanned aerial vehicle and a shooting time parameter, carrying out deblurring processing and splicing processing on an initial blade image collected in dynamic inspection of the unmanned aerial vehicle to obtain a second image; performing feature extraction on the second image by adopting an SIFT feature matching algorithm to obtain a third image; and performing defect detection on the third image by adopting a dual-channel convolutional neural network. The initial images are subjected to motion compensation and splicing, the blurring effect caused by blade rotation can be eliminated, and the problem of image dislocation in dynamic shooting is solved. According to the method, feature extraction and defect detection are carried out on the basis of the images after motion compensation and splicing are completed, the dynamic inspection efficiency and the defect detection rate are remarkably improved, and efficient and accurate inspection of the fan blade is achieved when the fan is in the running state.

The invention relates to a space alignment method based on a combined camera device of a traditional camera and an event camera, and belongs to the technical field of camera devices. According to the space alignment method, when event stream data generated by the event camera is processed, a deblurred event frame is generated by projecting and compensating an event; and the definition of the event frame image and the scene detail characterization capability are improved. And carrying out downsampling and edge gradient image calculation on a frame image of the traditional camera. And finally, carrying out normalized cross-correlation sliding matching calculation on the event image and the frame image to obtain a sub-pixel-level precision disparity map, and realizing high-precision space alignment of the traditional camera and the event camera.

The invention discloses a computer vision image processing method based on artificial intelligence, which relates to the technical field of computer image processing and comprises the following steps: synchronously acquiring visible light, infrared thermal images, vibration spectrum and reference temperature data to ensure time-space consistency; a dominant frequency and a direction angle are extracted based on a vibration spectrum, a visible light image is directly driven to be deconvolved and deblurred, and edge details are recovered; performing dynamic weight fusion on the edge gradient map of the deblurred image and the normalized temperature difference map to generate a dual-channel high signal-to-noise ratio feature map; performing cross validation on the defect candidate region through fractal dimension and a heat conduction rule, and generating a high-confidence mask; and the direction angle is dynamically fed back and adjusted based on the mask edge fracture proportion, iterative optimization is performed until the defect edge continuity reaches the standard, and the method adapts to the complex vibration working condition.

The invention discloses an image deblurring method and system, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a to-be-deblurred image; inputting the to-be-deblurred image into a pre-constructed deblurring model to obtain a deblurred image; the deblurring model is constructed and obtained by respectively adding feature fusion extraction modules in front of a down-sampling module in an encoder of the U-Net model and in front of an up-sampling module in a decoder on the basis of the U-Net model. According to the method, the global context, the directional details and the channel correlation of the image can be efficiently modeled, and the deblurring processing can be effectively performed on the image.

The invention provides an image deblurring method based on amplitude, phase and time domain channel fusion, a network adopts a U-shaped architecture, an adaptive amplitude and phase compensation strategy is introduced, and information weights of different frequency bands are dynamically adjusted on a channel level; the method comprises two stages and five steps: a training stage: S1: in the training stage, obtaining an image deblurring data set and preprocessing an image; s2, constructing an image deblurring model based on amplitude phase and time domain channel fusion; s3, optimizing a loss function; s4, training and constructing an image deblurring type based on amplitude phase and time domain channel fusion; and S5, performing performance evaluation on the training model in the test stage. According to the method, the time domain and frequency domain feature enhancement strategies are combined, the image definition is recovered, meanwhile, the influence of low resolution and blurring is effectively reduced, and therefore the overall performance of a deblurring task is improved.

The invention discloses a low-illumination blurred image 3D scene reconstruction method based on Gaussian sputtering, and the method comprises the steps: S1, building a progressive iteration enhancement frame, and setting a middle brightness anchor point between low-light observation and target brightness; s2, generating a plurality of enhanced images based on the intermediate brightness anchor points in combination with histogram equalization and gamma correction technologies; s3, carrying out rapid deblurring processing on the enhanced image; s4, constructing a scene representation model based on 3D Gaussian sputtering, and performing explicit estimation and noise suppression in combination with a noise sensing module; s5, taking the reconstructed rendered image as the deblurring prior of the enhanced image of the next brightness level so as to execute deblurring processing operation, and performing iterative optimization until the target brightness is reached; and S6, generating a high-quality new view angle image based on the finally reconstructed 3D scene. According to the method, the rendering speed is greatly improved while the reconstruction quality is ensured, real-time three-dimensional reconstruction is realized, and the problem of noise amplification in a low-light environment is effectively solved.

The invention discloses an intelligent paper marking system based on image cutting and recognition, belongs to the technical field of intelligent paper marking, aims at solving the problem that the answer quality of students cannot be comprehensively and accurately evaluated, and comprises an image acquisition module which is used for acquiring test paper images, automatically adjusting scanning parameters according to the size and color mode of test paper, supporting multi-angle scanning and acquiring the test paper images; the image preprocessing module comprises an image enhancement unit, a tilt correction unit and a binarization processing unit and is used for carrying out brightness and contrast adjustment, deblurring, tilt correction and binarization processing on the acquired image; through the image cutting, character recognition and semantic understanding technologies, the answer content, including complex handwritten fonts, altered characters and the like, on test papers in various formats can be accurately recognized, misjudgment caused by non-standard formats or writing problems is avoided, the scoring result is more objective and accurate, and the real answer level of students can be better reflected.

The invention discloses a monocular vision image deblurring method for a low-altitude miniature unmanned aerial vehicle, which belongs to the field of miniature unmanned aerial vehicle image processing, and comprises the following steps: preprocessing a blurred image to obtain a preprocessed blurred image and a blurred kernel estimation result; constructing a deep reinforcement learning model based on an attention mechanism and memory playback, and dividing the preprocessed blurred image into overlapped sub-image blocks to obtain a plurality of blurred image blocks and corresponding blurred kernel blocks; inputting the blurred image blocks and the corresponding blurred kernel blocks into a deep reinforcement learning model, performing channel-level fusion on the attention weight map, the blurred image blocks and the blurred kernel blocks, performing deblurring processing according to a dual-network structure, and outputting deblurred sub-blocks consistent with the input size; and based on the deblurred sub-blocks, eliminating the discontinuity of the boundaries of the sub-blocks through weighted fusion of overlapped regions to obtain a reconstructed image. According to the method, the problem of edge fault easily occurring in a traditional blocking method is solved, and the processing speed and the image quality in a complex scene are both considered.