1303 results about "Edge detection" patented technology

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

The invention discloses an electric power system safety early warning method and system based on multi-modal cooperation, and relates to the technical field of electric power system safety early warning, and the method comprises the steps: collecting multi-source operation data, carrying out the preprocessing, carrying out the multi-modal feature extraction and fusion based on the preprocessed data, and carrying out the multi-modal feature extraction and fusion. Inputting an edge detection model and outputting an abnormal confidence score in combination with an attention mechanism; and performing alarm grading according to the abnormal confidence score, constructing a causal diagram for alarms with high risk levels in combination with associated security events, and performing future attack path prediction by adopting a time sequence diagram neural network. According to the method, multi-scale convolution and a channel attention mechanism are fused, the extraction capability of the multi-source data time sequence features of the power system is enhanced, the anomaly detection precision is improved, dynamic attack path prediction is realized in combination with RMTPP and causal atlas topological constraints, sequence modeling is enhanced through self-attention and position coding, and the detection accuracy is improved. And the perspectiveness and the reliability of the safety early warning of the power system are obviously enhanced.

The invention provides a multi-step automatic testing method based on machine vision, and the method comprises the steps: capturing an original image sequence of a test scene through a camera, extracting the contour features of a test object in an image through an edge detection algorithm, and obtaining a preliminary positioning coordinate; if the dynamic position change trend exceeds a preset threshold value, adjusting an image enhancement parameter to suppress background noise, and obtaining an enhanced target image; feature matching is carried out through the enhanced target image, a robust identifier such as a texture mode is extracted, and an accurate three-dimensional position coordinate is obtained; according to the accurate three-dimensional position coordinates, calculating an execution deviation value of the current step, and judging whether the deviation value is within an allowable range or not; if the deviation value is within the allowable range, a control instruction sequence is generated and transmitted to the mechanical arm, and a step execution confirmation signal is obtained; comparing a confirmation signal with a next image sequence through the steps, updating positioning model parameters, and determining a continuous adjustment scheme of the whole test process.

The invention relates to the technical field of semiconductor manufacturing, in particular to a wafer pre-alignment device and method, and the device comprises a mounting platform, an XY motion platform, a rotating platform, a suction cup, and an edge detection assembly. The method comprises the steps of device correction, coarse scanning and circle center positioning, fine scanning and notch accurate positioning and alignment execution. Through software and hardware cooperation, online self-correction is used for eliminating system errors, high-order fitting and feature screening algorithms are used for restraining random errors and model errors, the repeated positioning precision, notch orientation precision and equipment consistency of the wafer pre-alignment device are improved, and the high-precision requirement for a wafer transmission system under the advanced manufacturing process can be met.

The invention provides a wafer back surface and edge detection device which comprises a base, and a rotary lifting module, a clamping lifting module, a motion direction conversion module and a visual detection module which are mounted on the base, the rotary lifting module comprises a rotary mechanism and a lifting mechanism; a supporting part for placing a wafer is arranged in the lifting mechanism, and the lifting mechanism is configured to jack the wafer up / down to a working position; the rotating mechanism is configured to provide a circumferential direction movement trend for the movement direction conversion module; the motion direction conversion module is arranged above the rotary lifting module and the clamping lifting module, and is driven by the clamping lifting module to realize displacement in the vertical direction, and the lifting motion of the clamping lifting module is converted into radial displacement of the motion direction conversion module; and the visual detection module comprises a camera unit, the camera unit faces the wafer, and the visual detection module is configured to perform image acquisition and detection on the wafer.

The invention relates to the technical field of remote sensing geological prediction, in particular to a slope support stability prediction method and system based on remote sensing data, and the method comprises the steps: obtaining a multispectral remote sensing image of a target slope region, and generating a vegetation mask through employing a normalized vegetation index; removing interference pixels of a vegetation coverage area in combination with morphological filtering and connected area analysis, then identifying support structure features through an improved edge detection algorithm, delimiting an influence area by adopting a region growing algorithm based on machine learning, inputting an area image into a convolutional neural network model, and obtaining an image of the support structure; a deformation probability graph is output by using a data enhancement technology and a weight optimization layer, finally, a threshold value is determined according to historical data, slope support stability categories are divided in combination with spatial neighborhood information and a voting mechanism, vegetation interference is effectively eliminated, a support structure is accurately recognized, prediction precision is improved, slope support stability can be accurately judged in time, and the method is suitable for popularization and application. And a reliable basis is provided for early warning and protection of slope disasters.

The invention discloses an autoclaved aerated concrete member surface defect intelligent identification system based on image processing, and particularly relates to the field of defect identification, comprising an image acquisition module, an image preprocessing module, a defect candidate region extraction module, a defect identification and classification module, and a result output and alarm module; according to the method, a high-definition industrial camera is used for collecting a component surface image, and adaptive median filtering and a Retinex algorithm are adopted for image denoising and enhancement, so that the influence of noise and uneven illumination is eliminated; utilizing an improved multi-threshold segmentation and Canny edge detection algorithm to accurately extract a defect candidate region; the method comprises the following steps: extracting three types of feature parameters of shape, texture and gray scale, and inputting the three types of feature parameters into a deep learning model taking ResNet50 as a basic network to realize automatic identification and classification of four types of typical defects of cracks, holes, unfilled corners and surface peeling; and finally, the system divides severity levels according to the defect size, and triggers differentiated visual alarm and linkage control.

The invention relates to the field of semiconductor processing, and discloses a semiconductor processing contour information extraction method, device and equipment and a storage medium, and the semiconductor processing contour information extraction method comprises the steps: obtaining a to-be-processed TEM graph and a design layout of a to-be-detected wafer; searching and determining a local layout corresponding to the TEM graph to be processed in the design layout; according to the contour distribution position in the local layout, determining a corresponding preliminary screening contour imaging area in the TEM graph to be processed; and contour edge detection is carried out according to the pixel value of each pixel point in the preliminary screening contour imaging area, and contour information of the processing contour of the to-be-detected wafer in the to-be-processed TEM graph is obtained. According to the method, the accuracy and reliability of contour information extraction in the TEM graph are realized by means of the design layout of the wafer to be tested, and a reliable data basis is provided for analysis of semiconductor process quality.

The invention discloses a fractal mask bilinear interpolation-based checkerboard angular point positioning method, which comprises the following steps of S1, acquiring a checkerboard image by using image acquisition equipment, and extracting candidate angular points in the checkerboard image; s2, determining a direction angle of the edge of the local area by using an edge detection algorithm and a polar coordinate transformation and clustering algorithm; s3, based on the direction angle of the edge of the local area, using a gray integral method and a least square method to preliminarily position the coordinates of candidate angular points; s4, in the neighborhood of the preliminarily positioned candidate angular point coordinates, obtaining a sub-pixel-level gray value by using fractal mask bilinear interpolation, and calculating local gradient distribution; s5, dynamically generating a weight mask based on a fractal theory, constructing a central point minimum error function according to a corner local gradient consistency principle, iteratively optimizing candidate corner coordinates, and outputting a sub-pixel level position; according to the method, the sub-pixel-level accurate positioning of the checkerboard angular points is realized by fusing the edge detection, the gray integration and the iterative optimization of the fractal theory.

The invention relates to the technical field of medical equipment, and provides a multi-modal lymphedema assessment and surgical navigation system based on image processing, which comprises a 3D scanning modeling module used for acquiring three-dimensional point cloud data of the body surface of a patient through structured light 3D scanning equipment, an iconography examination analysis module used for performing U-Net image segmentation on CT / MRI image data, and an image processing module used for processing the CT / MRI image data. The ultrasonic result calculation module is used for carrying out Canny edge detection on an ultrasonic image to determine the boundary of a surgical site, and the data integration and navigation generation module is used for realizing multi-modal data fusion through mutual information maximization, generating a surgical path based on FMM and carrying out real-time navigation in combination with an AR technology. According to the method, objective evaluation of lymphedema and accurate navigation of the operation are realized through accurate integration of multi-modal data and an image processing technology, the diagnosis and treatment accuracy is effectively improved, the operation risk is reduced, complications are reduced, and a scientific basis is provided for personalized treatment.

The invention provides a door opening and closing recognition method and system based on AI video analysis, and the method comprises the steps: reading a video file, collecting video data, carrying out the data preprocessing, obtaining a to-be-recognized image, carrying out the door frame recognition of the to-be-recognized image based on edge detection and Hough transform, obtaining a door body region ROI, carrying out the expansion of a detection frame, and obtaining a door body region ROI; key frame extraction is carried out according to the extended detection frame, and motion track characteristics of the door leaf are captured; constructing and training a dynamic state recognition network in combination with a YOLOv11 algorithm and deformable convolution to obtain a target dynamic state recognition model, recognizing motion track features, outputting to obtain an opening and closing motion state of a door, extracting an angle sequence of a recent preset frame number to obtain a plurality of feature values, and inputting the feature values to a time sequence model to obtain a time sequence model; and obtaining a triggering scene which causes the door state to change. According to the invention, generalization identification of opening and closing states of various door types can be realized, continuous angle detection is supported, and the accuracy and efficiency of door state identification are improved.

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 a two-dimensional image and geometric constraint-based cargo stack three-dimensional pose detection method and device, which are applied to the technical field of computer vision and logistics automation, and comprises the following steps of: through an edge straight line screening strategy, combining robust fitting of a random sampling consistency algorithm, calculating a three-dimensional pose of a cargo stack; a real physical boundary can be effectively and accurately recognized from complex visual interference such as adhesive tape, labels and printed patterns on the surface of a container, the accuracy and robustness of edge detection are remarkably improved, and therefore the high precision of final three-dimensional pose calculation is guaranteed; and secondly, only a common industrial camera is needed, and an expensive three-dimensional sensor such as a laser radar or a depth camera is not needed, so that the hardware cost and the maintenance complexity of the system are greatly reduced, and the cost benefit is extremely high. And thirdly, an efficient image processing and geometric calculation algorithm is mainly adopted, the calculation complexity is low, and the requirement for real-time detection of the goods stack on an industrial automatic assembly line can be met.

The invention relates to the technical field of automobile part quality detection, and discloses a skylight guide rail defect visual detection method and system.The skylight guide rail defect visual detection method comprises the steps that multi-light-source cooperative illumination configuration is adopted for image collection and self-adaptive fusion processing; guide rail areas are identified through edge detection, and differentiation enhancement processing is carried out on all the areas; multi-scale features are extracted, and key features are enhanced through an attention mechanism; extracting a defect candidate area, performing nearest neighbor matching to judge a defect type, and updating a prototype vector according to a quality inspector feedback increment; a visual generation and human-computer interaction interface is adopted to display a detection result and output a quality evaluation report; according to the invention, the technical problems of difficult imaging of the high-reflection metal surface, inconsistent quality of complex structure images, difficult identification of multiple types of defects and low real-time detection and calculation efficiency are solved, and the real-time detection requirement of a production line is met.

The invention relates to the technical field of wiring robot system fault detection, in particular to a transformer wiring robot system fault intelligent detection method. The method comprises the following steps: acquiring an image in a wiring process and graying the image to obtain a grayscale image; obtaining candidate pixel points in the grayscale image and a pixel point sequence of the candidate pixel points; acquiring a local gradient amplitude change feature value and a local gradient direction change feature value of each neighbor pixel point, and further acquiring initial edge feature credibility of the candidate pixel points; obtaining the final edge feature credibility based on the initial edge feature credibility of each marked pixel point of one candidate pixel point; performing clustering analysis based on the final edge feature credibility of each candidate pixel point in a gray level image to obtain a low threshold value and a high threshold value of the gray level image; and carrying out edge detection on the grayscale image by using the low threshold and the high threshold, and identifying the abnormity of the wiring robot. According to the invention, the wiring abnormity of the wiring robot can be effectively processed.

The invention belongs to the technical field of oil and gas drilling well control equipment, and particularly relates to a ram blowout preventer state confirmation method with a locking indication window, which comprises the following steps: arranging a transparent observation window on a blowout preventer shell, and calibrating the alignment position of the end face of a locking shaft and an inner side reference scribed line; after the locking action is executed, the alignment state is confirmed through multi-angle visual observation; a switchable auxiliary light source is integrated to eliminate visual interference; an industrial camera is used for collecting images, displacement deviation is recognized through an edge detection algorithm, and an alarm is given if the displacement deviation exceeds the limit; and generating an encrypted digital report containing a timestamp and equipment information, uploading the encrypted digital report to a monitoring platform, and supporting real-time check and electronic signature of a mobile terminal. Through three layers of mechanisms of standardized vision-mechanical mapping, optical enhancement and digital perception, high-reliability, high-compatibility and traceable locking state confirmation is realized.

The invention belongs to the technical field of cutting beds, and relates to a vision-based cloth paving machine cloth tightness self-adaption method, which comprises the following steps of S1, acquiring an image video stream on the side surface of a cloth roll on a cloth paving machine in real time through an industrial camera; s2, extracting a single-frame image from the video stream, and identifying the cloth type of the current cloth roll; s3, preprocessing the extracted image: segmenting a region of interest on the side surface of the yardage roll, and removing wrinkle interference in the image; s4, carrying out edge detection on the preprocessed region of interest, and extracting the contour of the side surface of the yardage roll; s5, identifying and fitting the contour as a circle, and calculating the diameter of the circle; and S6, determining a corresponding tightness parameter according to the diameter. According to the cloth paving machine cloth tightness degree self-adaption method based on vision, self-adaption control over the cloth tightness degree in the cloth paving process can be achieved, and the cloth paving quality is improved.

The invention discloses a ceramic substrate surface defect intelligent identification method and system based on multispectral imaging, and the method comprises the following steps: synchronously collecting multispectral images under a plurality of visible light and near-infrared wavebands, constructing a multispectral image stack, and completing dark field deduction, registration and geometric correction processing; inputting the crack sensitive wave band combination image into a CrackFormer crack identification network, extracting slender structure characteristics and positioning a crack area; inputting the edge enhancement graph into an improved BDCN edge detection network, extracting the contour structure of the ceramic substrate, and identifying the position of a contour gap; a chromatic aberration defect area is extracted through brightness difference calculation; and fusing the identification results of the three types of defects, and outputting a defect mask pattern and structured defect information. According to the method, the multi-band image and the deep learning model are fused, and the accuracy and adaptability of ceramic substrate defect detection are improved.

The invention discloses a labeling quality intelligent detection method and system for multiple materials, and the method comprises the steps: S1, collecting original image data of paper, plastic and metal labels through an industrial camera, and carrying out the preprocessing, and obtaining standardized image data; s2, designing an improved edge detection network, extracting an initial edge feature of the label, generating a linear edge feature through curve fitting, calculating a label rotation angle, and executing rotation transformation to obtain image data after rotation correction; s3, constructing a double-branch network comprising material and defect branches, extracting material types, generating defect features in combination with the material types, and finally generating structured defect features; and S4, according to the structured defect features, generating material tolerance, and then calculating a quality score and a quality grade. According to the method, the problems of material misjudgment and defect detection omission caused by low rotation correction precision of the special-shaped label and staged processing of multi-material defect detection in a traditional method can be solved.

The invention relates to the technical field of image processing, and discloses a mechanical part contour extraction method and system based on edge detection. The method comprises the following steps: carrying out overlapping region division on a part image, implementing adaptive illumination compensation according to gray level statistics, carrying out gradient joint detection on a preprocessed image through adaptive double thresholds, screening reliable edge points according to neighborhood gradient direction deviation, continuously carrying out contour tracking connection according to a gradient direction, and carrying out contour tracking connection according to the gradient direction; and realizing inner and outer contour layered identification according to curvature statistical characteristics and topological nesting depth. The integrity of contour extraction of the mechanical part and the accuracy of layered recognition are improved.

The invention discloses multifunctional wafer detection equipment. The equipment comprises an edge detection module, a robot, two sets of macroscopic detection units arranged in parallel and a microcosmic detection assembly. The robot is arranged on the side of the edge detection module to transfer the wafer. Each macroscopic detection unit comprises a transfer mechanism, a wafer rotating and transferring mechanism, an external clamping mechanism, an internal clamping mechanism and a photographing module. Wherein the transfer mechanism is used for receiving wafers from the robot; the working range of the rotary wafer transfer mechanism covers the transfer mechanism, the external clamping mechanism, the internal clamping mechanism and the microscopic detection assembly, and is responsible for efficiently transferring wafers among the transfer mechanism, the external clamping mechanism, the internal clamping mechanism and the microscopic detection assembly; the external clamping mechanism and the internal clamping mechanism which are adjacently arranged can carry out wafer handover; and the photographing module is used for photographing the wafer fixed by the internal clamping mechanism. By optimizing layout and automatic circulation, efficient integration and continuous proceeding of wafer edge detection, macroscopic detection and microcosmic detection are achieved, and the detection efficiency and the automation level are remarkably improved.

The invention discloses an X-ray image artifact definition automatic correction and enhancement method based on deep learning, and belongs to the technical field of image artifact correction, and the method specifically comprises the steps: obtaining an X-ray image, employing frequency domain decomposition and edge detection to generate an artifact image and a sharpness image, and extracting an imaging parameter set and an anatomical region label; establishing a parameter-driven artifact migration network, inputting an imaging parameter set and an artifact graph, learning an artifact vector field and phase prior, and forming reversible representation of an artifact source; constructing a double-branch decoder, dissecting branches to generate a structural skeleton diagram, and performing cross attention fusion to obtain a candidate correction field; according to the candidate correction field, geometric remapping and sub-band compensation are carried out on the image to obtain an intermediate correction result image, and an artifact vector field is sent to a feedback loop; and the consistency of the sharpness graph and the structural skeleton graph is taken as a loss item, artifact residual constraint is combined, a correction field and decoder parameters are optimized, and non-anatomical textures are suppressed.

The invention discloses edge detection equipment with an alignment function, and belongs to the field of semiconductor manufacturing equipment.The equipment comprises an edge detection module, a straightening mechanism of the edge detection module comprises a suction cup, a rotating motor, a two-dimensional motion module and an edge searching camera, the two-dimensional motion module is composed of an X-axis guide rail, a Y-axis guide rail, a sliding plate and a motor, and wafer translation and rotating alignment are achieved; the optical assembly comprises a camera, a prism assembly, a transverse movement module and a camera fine adjustment mechanism, the prism assembly captures light rays at the upper edge and the lower edge of a wafer through an upper prism and a lower prism, and the light rays are integrated through a 90-degree turning prism to form a composite image containing multi-part information; wafer alignment, edge detection and identification code reading can be completed synchronously, the detection precision and efficiency are improved, and the problems that in an alignment and detection separation mode in existing semiconductor manufacturing, independent detection equipment needs to be additionally configured, the wafer processing time is long, damage is likely to happen after multiple times of transmission, and the yield is low are solved.

An example method of video coding includes receiving a video bitstream comprising a plurality of frames, including a current frame comprising a current block. The method also includes determining an edge direction by performing edge detection for the current block, and determining a difference between a current sample of the current block and a neighboring sample based on the edge direction. The method further includes applying a filter to the current block based on the determined difference.

The invention discloses a robot real-time visual positioning method for a dynamic shielding environment, and the method comprises the steps: collecting an image, completing the time synchronization and camera calibration, and generating a semantic segmentation and edge detection result; visible and shielding double-channel sparse reconstruction is executed, a shielding geometric shell layer is generated, and a holographic body is formed; extracting geometric tokens, semantic tokens and shielding tokens, and inputting the geometric tokens, the semantic tokens and the shielding tokens into a gating multi-mode Transform for game type fusion; constructing a partition factor graph, and adding a re-projection factor, a mutual exclusion shielding factor and a zero observation retention factor; setting completely-shielded, partially-visible and semitransparent agents, independently optimizing in the factor graph, and fusing results through consensus factors; and outputting the six-degree-of-freedom pose and the uncertainty, and updating the shielding holographic body and the sliding time window. According to the method, high-precision real-time positioning of the robot in a dynamic shielding environment is realized through shielding holographic body modeling and multi-modal consensus optimization.

The invention discloses an EAGLE-Net remote sensing image segmentation method, which comprises the steps of extracting multi-scale features of an input image, and obtaining low-level features of spatial details and high-level features of semantic information; the low-level features of the space details and the high-level features of the semantic information are input into an attention gating module, space-channel two-dimensional attention weights are generated, and weighting processing is conducted on the low-level features of the space details and the high-level features of the semantic information; inputting the weighted high-level features into a dynamic void space pyramid, predicting multiple groups of void rates based on global information, and generating enhanced semantic features through multi-scale void convolution fusion; splicing and decoding the weighted low-layer features and the enhanced semantic features to obtain a segmented prediction map; and performing edge detection on the segmented prediction map to generate an edge prediction map. According to the method, noise can be suppressed, key signals can be enhanced, large targets and small targets can be adaptively covered, and object boundaries can be accurately recovered by means of edge supervision.

Systems, apparatus, and methods for post-processing video e.g. frame denoising. Noise reduction techniques may be employed to improve the quality of digital video. Frames may be extracted from a video. Synthetic frames may be created using motion data between the extracted frames. Synthetic frames may be masked to exclude pixels from the composite frame. Thresholds used in masking may vary based on the temporal distance of the extracted frame used to create the synthetic frame and the extracted frame. Masking may be based on frame differences between extracted and synthetic frames (e.g., sub-pixel / luminance differences), areas of lower quality motion data (e.g., occlusions), or edge detection in the extracted frames. Synthetic and extracted frames may be composited generating frames having less noise. The composited frame may be based on averaging pixel values across the synthetic and extracted frames. Composited frames may be compiled and encoded into denoised video.

The invention provides a PWM signal generation circuit and a control device. The PWM signal generation circuit comprises a control module, a delay chain module and an edge detection module, wherein the delay chain module and the edge detection module are electrically connected with the control module; the edge detection module is used for receiving an original PWM signal output by the PWM system, detecting the edge of the original PWM signal based on an edge detection instruction, generating an edge indication signal and sending the edge indication signal to the delay chain module; the delay chain module comprises a plurality of delay units and is used for delaying the edge of the original PWM signal by target delay time to generate a target PWM signal; according to the invention, the delay operation is carried out on the original PWM signal at the specified edge through the delay chain module, the target PWM signal with the resolution higher than that of the original PWM signal is obtained, and the high-resolution PWM signal is generated by using the low-frequency clock of the PWM system on the premise of not changing the system work clock period of the PWM system.