798 results about "Motion blur" patented technology

The invention provides an unmanned aerial vehicle identification method and system for low-altitude security and protection. An optical compensation parameter set for unmanned aerial vehicle imaging optimization is generated in real time through a joint optimization model of an ambient light intensity change rate and a background motion vector field, and an original optical sequence in a target capture window is processed by using the parameter set. And constructing a time domain deconvolution kernel in combination with the motion characteristics of the unmanned aerial vehicle to generate an enhanced optical image resistant to motion blur. Non-linear weighted fusion is carried out through a disturbance intensity evaluation function, and a confrontation disturbance feature mask is formed. The enhanced optical image and the confrontation disturbance feature mask are subjected to airspace superposition operation, a multi-scale residual network is adopted to carry out target confidence estimation on the superposed image, an unmanned aerial vehicle recognition result is generated, and the unmanned aerial vehicle recognition accuracy and the anti-interference capacity in the low-altitude complex environment are remarkably improved through the technical scheme provided by the invention.

The invention discloses an unmanned aerial vehicle laser and vision fusion inspection method and system for bridge bottom disease detection, and the method comprises the steps: carrying out the synchronous data collection through employing a calibrated laser radar, a camera and an IMU, and obtaining a three-dimensional laser point cloud and a two-dimensional visual image of the appearance of a bridge; sharpening the image containing the motion blur and completing brightness self-adaption of the image; stable feature points are extracted, multi-frame matching is carried out, the corresponding poses of the images are estimated, and bridge dense point cloud reconstruction is completed; performing geometric component segmentation on the point cloud to generate a geometric prior region; component segmentation is carried out on a support area in the image, and a continuous and accurate component segmentation result is obtained in combination with a geometric prior area; screening the image, calling a targeted disease detection model in a corresponding component area, and generating a segmentation mask for the disease; obtaining a real disease three-dimensional point cloud, and carrying out quantitative calculation on the physical size of the disease; and displaying the real disease three-dimensional point cloud data and the physical size of the disease. The method is high in efficiency and precision.

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 a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

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

The invention provides an image noise mark feature selection method and system, a storage medium and a computer. The method comprises the steps of obtaining a to-be-processed image noise mark data set; embedding a sample set in the image noise mark data set into a multi-granularity fuzzy cluster to construct a dynamic fuzzy membership evaluation matrix; dynamically evolving a multi-level high-precision granular ball cluster; obtaining mark distribution with high identification degree; constructing a rough perception feature evaluation framework based on granular ball topology driving, extracting decision equivalence classes by combining rough set upper and lower approximation and extended positive domain models, and determining and measuring the contribution degree of each feature to a decision system by fusing multi-granular-ball decision boundary information based on a dependency degree quantitative model; a particle and ball structure consistency verification mechanism is introduced, and multi-level evaluation is carried out on the importance of the features through dependency and consistency. According to the method, the optimal feature subset with strong anti-noise performance and high discrimination capability is obtained, and stable and efficient input support is provided for a subsequent image noise mark learning model.

The invention provides a textile pattern dislocation identification method, which comprises the following steps of: performing geometric transformation analysis on extracted pattern contour information, calculating a relative position relationship of each part of a pattern, establishing a pattern moisture deformation model, and judging whether the pattern is dislocated or deformed by comparing the difference between the current pattern and a reference model; on the basis of the corrected pattern, adding a special waterproof mark on the surface of the wet textile, manufacturing a microscopic mark which does not influence the fabric performance, and capturing the position of the waterproof mark through infrared imaging equipment to serve as an auxiliary reference point for pattern positioning; and based on the waterproof mark position of the pattern, tracking the motion trail of the waterproof fabric, calculating the image distortion degree according to the motion vector field, correcting the moving waterproof mark fabric image in real time, and eliminating the influence of motion blur on waterproof mark identification.

The invention provides a fuzzy barcode image processing method and system fused with super-resolution repair. The method comprises the following steps: acquiring a bar code image containing dynamic blurring, detecting a blurred region formed by superposition of motion tracks in the bar code image based on space-time continuity characteristics of the dynamic blurring, extracting an edge diffusion direction of the blurred region, and determining the dynamic blurring of the bar code image according to the edge diffusion direction. And performing multi-scale detail layer generation on the fuzzy region, inputting the multi-scale detail layer into a fusion repair module, and generating a repaired high-resolution barcode image by alternately executing super-resolution reconstruction guided by local high-frequency information and morphological repair of a module gap. Edge sharpening and noise suppression are carried out on the high-resolution barcode image, and a clear image meeting the barcode decoding standard is output; according to the technical scheme provided by the invention, the restoration precision and reliability of the complex dynamic fuzzy scene are remarkably improved, and the output image strictly meets the geometric constraint requirement of bar code decoding.

The invention provides a single particle diffusion quantitative feature prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target input image corresponding to a target single particle; based on a pre-trained particle trajectory prediction model, according to the target input image, predicting to obtain a particle pseudo trajectory image; on the basis of the particle pseudo-trajectory image, diffusion quantitative characteristics of the target single particle are calculated; wherein the particle trajectory prediction model is obtained by performing training optimization according to a first training sample set formed by the single-particle motion blurred image and the corresponding particle motion trajectory image. According to the method, the actual movement path of the target single particle is predicted from the target input image through the particle trajectory prediction model, and the diffusion quantitative characteristic of the target single particle is calculated based on the actual movement path, so that the diffusion coefficient of a large number of single particles can be rapidly and accurately predicted in a high-mark-density environment, and the phototoxicity is remarkably reduced; diffusion characteristics of different biological particles can be effectively distinguished, and a powerful tool is provided for dynamic research of particles in a diffusion system and living cells.

The invention relates to the technical field of multi-modal sensing, and discloses a multi-modal large model detection and recognition robot recognition system for a complex scene, the system constructs a dynamic manifold modeling module, realizes cross-modal joint denoising through a stochastic differential equation and depth score matching, constructs a drift term and an anisotropic diffusion term by using an optical flow field, and realizes multi-modal detection and recognition of a multi-modal large model. Dynamic noise interference such as rain fog and motion blur is eliminated; on the basis, designing an information geometric alignment module, and based on Riemannian manifold optimization and orthogonal projection matrix calculation, realizing geometric equidistant mapping of vision-Li DAR features through multi-scale measurement tensor fusion; a dynamic external parameter calibration module is further provided, SE (3) manifold Kalman filtering is combined with a noise self-adaptive scaling technology, and external parameter offset is tracked and compensated in real time. Compared with a traditional method, the method has the advantages that the core problems of cross-modal data geometric mismatch, external parameter drift accumulation, low semantic fusion efficiency and the like are solved, and the sensing precision and robustness of the automatic driving system in a complex dynamic scene are remarkably improved.

The invention provides a power transmission working vehicle detection method and system based on a frame sequence, and the method comprises the steps: carrying out the frame division of a target video related to a power transmission working scene, so as to form multiple frames of image data, carrying out the depth extraction, so as to obtain depth features, carrying out the time fusion of the depth features corresponding to each frame of image data, so as to obtain a fusion feature map, therefore, by combining the time sequence information of the multiple frames of images, the detection precision and robustness of the target under the conditions of shielding, motion blur and scale change are effectively improved, then target detection about the detected target is performed to obtain the candidate target frame and the confidence score corresponding to the candidate target frame, and the target detection accuracy is improved. According to the method, weighted average is carried out based on the confidence score to obtain a smoothing result score, and the smoothing result score is compared with a pre-obtained threshold to confirm a detection result of the detection target, so that dynamic confidence evaluation and adaptive threshold adjustment are carried out based on multi-frame information, single-frame false detection and missing detection can be remarkably reduced, and the detection stability is enhanced.

The invention relates to a multi-modal fusion corn harvester header height adaptive adjustment method and system, and belongs to the technical field of automation control, the method comprises the following steps: using a binocular camera to collect crop images, and preprocessing the crop images to obtain a depth map; the image is input into a YOLOV8n model, and coordinates of a center point at the bottom of a bounding box of the corncob are obtained through model operation; performing plane fitting on the depth map to obtain a ground height; calculating a corn ear height initial value based on the ground height and the center point coordinate; vibration data of working of the harvester is collected to perform vibration compensation on the initial height value to obtain a height correction value; and dynamically adjusting the header height of the harvester by adopting a dynamic fuzzy PID algorithm based on the correction value. According to the method, the target detection model and the dynamic fuzzy PID algorithm are combined, so that the problems of low visual detection precision and control response lag of existing agricultural machinery in a complex field scene are solved, and crop loss in harvesting operation is effectively reduced.

The invention discloses a lesion area self-adaptive segmentation method and system for a seminal vesicle endoscope image, particularly relates to the field of medical image processing, is used for solving the problems of geometric distortion and artifacts in the seminal vesicle endoscope image, and aims to eliminate geometric deviation caused by thick layer sampling through synchronous acquisition and attitude correction. Then, a resampling strategy is adjusted in a self-adaptive mode through key geometric features, the problems of inter-layer artifacts and resolution imbalance are effectively weakened, a lesion segmentation network is optimized through smooth regularization and geometric constraint, continuity and geometric accuracy of lesion boundaries are ensured, finally, the accurate lesion mask is dynamically overlaid to a real-time frame stream, and the real-time frame stream is obtained. A quantitative basis is provided for biopsy path planning and photodynamic dose scheduling; smooth and continuous images are completed and output in a strict time window, the perception ability of an operator to tiny pathological changes is enhanced, meanwhile, the method is suitable for various endoscope devices, motion blur and light spot artifacts are restrained, and focus details are kept clear.

The invention relates to the technical field of image processing, in particular to a moving image deblurring method based on multi-modal bidirectional fusion, which comprises the following steps of: firstly acquiring multi-modal data information of a moving blurred image, and then acquiring preprocessed event stream information which is compatible with image information and is in a three-dimensional voxel format based on the event stream information; performing multi-modal bidirectional fusion processing on the image information and the preprocessed event flow information to obtain fusion features of the motion blurred image; taking the fusion features as input, carrying out residual connection step-by-step learning through a residual connection module, and generating a residual sequence; and based on the residual sequence, obtaining a deblurred motion clear image through an event-based double integral physical model. According to the method, through a bidirectional fusion strategy from shallow to deep, the development of moving image deblurring is promoted, a new thought and method are provided for multi-modal data fusion, and technical support is provided for efficient application of other tasks such as automatic driving and target detection.

The invention relates to the technical field of computers, and discloses a real-time rendering and interaction method and system for an immersive virtual reality scene. The method comprises the following steps: fusing tuner inertial data and eyeball tracking data, and constructing a prospective state prediction model; generating a predictive focus field in combination with scene visual saliency; synthesizing an anisotropic temporal-spatial resolution graph according to the predicted head angular velocity; gPU variable-rate coloring is driven to realize non-uniform rendering; and re-projection or dynamic fuzzy correction is executed in a self-adaptive manner according to the attitude prediction error before display. According to the technical scheme, the perception delay and the rendering load are remarkably reduced, and the frame rate stability and the visual immersion in a high-dynamic scene are improved.

The invention relates to the field of feature measurement of porous materials, in particular to an identification and calculation method and device for pore features of a porous material and a readable storage medium, and the method comprises the steps: obtaining a gray image of the porous material; carrying out binarization operation on the grayscale image by adopting a multi-scale mixed threshold segmentation method driven by dynamic fuzzy logic to obtain a binarized image; performing refined identification and segmentation on the binarized image by using a multi-stage morphological communication refined segmentation method, and performing calculation to obtain a first pore feature parameter; performing refined identification and segmentation on the binarized image by using a Morse-Smell complex topological feature segmentation method based on morphology, and performing calculation to obtain a second pore feature parameter; calculating the percentage difference between the first and second pore characteristic parameters; if the absolute value of the percentage difference is greater than the preset value, recalculating the first and second pore characteristic parameters; otherwise, recording the average value as a final result. According to the method, the recognition precision and adaptability of the complex pore image are remarkably improved.

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 relates to the technical field of computer vision, and discloses a robot image recognition system based on a convolutional neural network, and the system comprises a dynamic retina blur sensing module which is used for estimating a pixel-level dynamic blur kernel parameter from an input blur image; the differentiable physical deblurring layer is used for performing feature reconstruction on the blurred image based on the dynamic blurring kernel parameters to generate deblurred image features; and the fuzzy robust CNN backbone network dynamically adjusts the voidage of a convolutional layer according to the dynamic fuzzy kernel parameters so as to adapt to the local fuzzy intensity. The problem of parameter distortion of a data-driven model is solved through an optical flow constraint fuzzy perception module, dilated convolution is dynamically adjusted based on fuzzy intensity to expand a high fuzzy region receptive field, an end-to-end joint training framework is constructed to realize classification error reverse optimization fuzzy kernel parameters, and a two-stage curriculum learning strategy is combined to improve the robustness of the system. The physical model is solidified first and then global optimization is carried out, and the bottleneck of traditional staged training is broken through.

A time-of-flight pixel circuit includes a photodiode configured to generate charge in response to modulated light reflected from an object. First and second transfer transistors are coupled to the photodiode. The first transfer transistor transfers a first portion of charge from the photodiode in response to a first modulation signal and the second transfer transistor transfers a second portion of charge from the photodiode in response to a second modulation signal. The second modulation signal is an inverted first modulation signal. A first floating diffusion is coupled to the first transfer transistor to receive the first portion of charge in response to a first modulation signal. Each one of a first plurality of sample and hold transistors is coupled between a respective one of a first plurality of memory nodes and the first transfer transistor.

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 motion blur removing method based on dynamic image interpolation, and the method comprises the steps: firstly correcting a blur track according to the dynamic change of time and direction; then, considering the stage speed change of the target, and carrying out weight updating on the initialized fuzzy kernel; then, under the condition that the target speed cannot be estimated, feature points are detected by using an SIFT algorithm, and the feature points are tracked by using an L-K optical flow method, so that a more accurate dynamic fuzzy kernel is constructed, and then deconvolution operation is performed by combining a neighbor interpolation technology, so that the definition of an original image is recovered; in order to further optimize the image quality, three image enhancement technologies, including histogram equalization, contrast enhancement and image sharpening, are combined to cope with visual interference in different environments, enhance the contrast and detail expressive force of the image, and further improve the image processing effect. Therefore, the unmanned aerial vehicle identification system can capture and identify the hostile target more accurately.

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 an astronomical positioning method for a space target elongated astronomical image of a large-field-of-view space-based monitoring platform, relates to the technical field of astronomical observation, and solves the problem that the elongated astronomical image is abandoned due to incapability of target association and precise orbit determination caused by inaccurate centering of the elongated astronomical image and low precision of a positioning method of an existing monitoring platform. According to the method, an observation image is divided into 16 sub-view fields, and each sub-view field is an independent view field and is calculated by an independent processor; removing fixed stars in the observation image by adopting an adjacent frame image difference method and extracting a space target star image; secondly, describing the length and angle of the lengthened astronomical image by adopting a minimum enclosing rectangle, and constructing a degradation model; performing motion blurred image restoration on the elongated star image; error points with large errors are eliminated by adopting a mutual comparison method, and track association of the space target is completed; and finally completing precise astronomical positioning of the elongated astronomical image and the like. According to the method, the processing efficiency is greatly improved, and a large number of lengthened astronomical images which are abandoned before are fully utilized again.

The invention relates to the technical field of electric power detection, in particular to an electric power inspection robot detection method and system based on machine vision. According to the method, the three-dimensional model of the target area is constructed by pre-collecting the basic data of the area, the historical defect data is obtained to mark the detection key area, the initial path and the key detection point are optimized by using the genetic algorithm, redundant collection points are removed, and the accuracy of the visual detection path is improved; meanwhile, visual data are analyzed in real time through a path acquisition collaborative analysis module, an optimization instruction is generated to dynamically adjust a path, and the problems of low efficiency and missing detection caused by independent operation of vision and navigation are avoided; the visual perception module collects detection dynamic data of definition, vibration acceleration, environment wind speed and environment light, the environment adaptability module analyzes and outputs a comprehensive fluctuation value, a corresponding fluctuation value interval is obtained, a corresponding environment adaptability regulation and control instruction is generated, and influences caused by motion blur and environment sudden change are avoided.

A video processing method includes: obtaining a plurality of image groups on the basis of a video frame sequence of an initial video; performing motion blur processing on the basis of each frame of image in a target image group, and fusing images which are obtained by performing motion blur processing on each frame of image, so as to obtain a motion-blurred image corresponding to the target image group; on the basis of a specified frame of image in the target image group, determining a main body object area and a background area, which correspond to the target image group; fusing the motion-blurred image with the specified frame of image according to the main body object area and the background area, so as to obtain a target fused image; and generating a target video on the basis of target fused images respectively corresponding to the plurality of image groups.

According to the video stream image optimization method and device provided by the embodiment of the invention, a dynamic fuzzy kernel generation mechanism is innovatively designed, and the data processing security is ensured through space-time decoupling and physical constraint. A content-motion double-branch network is constructed, and a reliable feature reconstruction system is established in combination with residual connection and space-time transformation. A physical constraint corrector is introduced, and high-quality video output is provided while user privacy is protected through adaptive fusion and rule correction. According to the method, the defects of the traditional technology in the aspects of fuzzy processing, feature decoupling, image optimization and the like are effectively overcome.

The invention relates to the technical field of computer vision, in particular to an image target detection system and method based on deep learning, and the system comprises a dynamic feature alignment unit, a motion blur compensation unit and a feature fusion control unit. A dynamic feature alignment unit generates spatial deformation parameters through a deformable convolutional layer and an offset prediction sub-network, resamples a shallow high-resolution feature map, and realizes deep and shallow feature space alignment, and a motion blur compensation unit generates a motion vector based on brightness gradient field difference, constructs a mask and weights a suppression blur region, so as to realize deep and shallow feature space alignment. The feature fusion control unit analyzes local entropy and target size distribution, dynamically distributes feature weights and feeds back and optimizes offset parameters, a closed-loop learning loop is formed by the method, and the problems of inaccurate feature alignment, fuzzy interference and poor scene adaptation are solved.

The invention relates to the technical field of industrial automatic detection, in particular to a collaborative visual detection method and system for an SMT production line, and the method comprises the steps: obtaining a beat reference signal which is synchronous with the periodic motion beat of a mounting head in the SMT production line; determining a stable acquisition window of which the vibration intensity is lower than a preset vibration threshold value; in the stable acquisition window, sending a synchronous trigger instruction to a plurality of visual inspection cameras deployed on the SMT production line in a unified manner; performing space-time alignment processing on the multi-path image data; performing motion blur correction processing on the image data subjected to the space-time alignment processing; and carrying out validity verification on the result of the space-time alignment processing, and executing a preset exception processing strategy when the verification result indicates that the verification fails. According to the invention, the beat reference signal synchronized with the motion beat of the mounting head is directly obtained, and the opportunity decision of image acquisition and the real-time vibration monitoring result are deeply bound, so that the double strategies of beat synchronization and vibration avoidance are realized.

The invention discloses a digital coding metamaterial forward-looking video imaging method and system based on sparse low-rank decomposition, and mainly solves the problems of motion blur and poor imaging stability in a non-sparse observation scene of a moving target in the prior art. According to the implementation scheme, echo data of N frames of observation scenes are collected; decomposing a scattering coefficient vector of an observation scene corresponding to each frame of echo into a low-rank vector of a background area corresponding to the observation scene and a sparse vector of a dynamic change area, and expressing a separation problem of the two change areas as a joint low-rank and sparse optimization problem; performing convex relaxation processing on the optimization problem, and converting the optimization problem subjected to the convex relaxation processing into an optimization problem in an unconstrained form to obtain a sparse low-rank decomposition-based metamaterial forward-looking video imaging model; and solving the constructed imaging model, and realizing multi-frame image reconstruction and video synthesis based on a solving result. According to the method, the imaging resolution and reconstruction stability of the non-sparse scene containing the moving target are improved, and the method can be used for intelligent security check and medical detection.

The invention provides a display panel and a display device. According to the embodiment of the invention, the pixel unit is divided into the first sub-pixel unit and the second sub-pixel unit to obtain the multi-domain structure, and the image display brightness of the second sub-pixel unit in the same pixel unit is smaller than that of the first sub-pixel unit in the display stage, so that the effect of low color cast under a large viewing angle is achieved; meanwhile, the control unit is arranged to control the conduction degree of the discharging switch in the second sub-pixel unit so as to achieve two-way selection of normal display and black frame insertion display, the black frame insertion function can be achieved, the motion blur phenomenon can be improved, and the structure is simple and easy to design.