314 results about "Deblurring" patented technology

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 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 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 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 present invention provides a system and method for utilizing multiple camera systems including at least three cameras, each camera having at least partly overlapping fields for removing blur in a moving object relative to a static background captured by the multiple camera system, comprising separating and extracting the moving object from the static background by isolating pixels corresponding to the moving object and distinguishing them from the static background by using images captured by the at least three cameras having field of views covering the moving object, enhancing clarity of the separated moving object by deblurring the separated moving, blurring the static background, reintegrating the deblurred separated moving object onto the blurred static background in the position of the images where the separated moving object was extracted.

The invention relates to the technical field of large language models, in particular to a method and system for cleaning large language model training pictures, comprising the steps of automatically checking and deleting damaged images, loading and processing images to delete blurred images, processing tasks in parallel and adopting streaming processing, deleting repeated images, and finally verifying and storing. The beneficial effects are that automatic cleaning of image data is realized, manual intervention is reduced, and processing efficiency is improved. Through operations of denoising, deblurring, format unification and the like, the image quality is remarkably improved, and the model training effect is enhanced. The computing power resource utilization rate is improved, and resource waste caused by low-quality data is avoided. The method is suitable for various large language model training scenes, and parameters and processes can be flexibly adjusted according to requirements.

The invention belongs to the technical field of image deblurring, and particularly relates to an edge deployment-oriented lightweight multi-path image deblurring method, which comprises the following steps of: 1, preparing a data set; 2, constructing a network model; step 3, training a network model; 4, optimizing the model and evaluating the performance; 5, finely adjusting the model; 6, storing the model; and step 7, hardware deployment and debugging. According to the method, the lightweight image deblurring network is designed, the performance of the model is improved while the parameters of the model are reduced, the lightweight deblurring network is used in the field of image deblurring, and the problems that an existing network model is large and actual deployment is difficult are solved; channel-level adaptive feature fusion is carried out on the feature map by designing a dual-response enhanced fusion network, and the contrast and significance of edge information are enhanced to realize efficient extraction of multi-level features; complementary optimization of global and local features is realized by designing a lightweight multi-path feature sensing network.

The invention provides an image visual identification processing method for foundation pit monitoring, and belongs to the technical field of data processing, and the method comprises the steps: 1, carrying out the multi-modal image and motion information collection of a foundation pit; 2, the background server carries out multi-modal feature fusion and blurred image screening on the foundation pit image; step 3, the background server performs rigid-non-rigid structure classification and feature enhancement based on the multi-modal feature map; 4, the background server performs dynamic fuzzy kernel construction and adaptive deblurring on the rigid structure area and the non-rigid structure area; and 5, based on the complete deblurred image, fine crack identification of the foundation pit is carried out. Through four core steps of multi-modal feature fusion extraction, dynamic fuzzy kernel construction, non-rigid structure enhancement and refined crack identification, efficient deblurring and accurate crack identification of a foundation pit image are realized.

The invention provides an electrical equipment defect identification method and device based on pose fusion, the method is applied to an identification device comprising a data processing module, a model construction module and a defect identification module, and the method specifically comprises the following steps: obtaining historical image data and corresponding defect information, and aligning pose data with the historical image data according to a shooting timestamp; performing perspective distortion correction and deblurring processing on the historical image data according to the corresponding pose data to obtain a corresponding equipment area image, and training a YOLO model in combination with the corresponding pose data and defect information to obtain a power equipment defect identification model; and constructing a recognition sample according to the currently collected image data and pose data of the power equipment, and inputting the recognition sample into the power equipment defect recognition model to obtain a defect recognition result of the power equipment. According to the method, the pose data can be fused into the pre-training data preprocessing stage and the training stage of the model, the method can adapt to defect identification of power equipment in different pose scenes, and the accuracy is guaranteed.

The invention relates to the technical field of bridge structures and vision measurement, and discloses a structure vibration displacement recognition method, device and equipment and a storage medium, and the method comprises the steps: obtaining a vibration video of a to-be-recognized region, and carrying out the preprocessing of the vibration video, and obtaining an image sequence and a displacement proportion; deblurring the image sequence by adopting a space-time coupling method to obtain a clear image sequence and an optimized optical flow matrix; performing intermediate frame interpolation based on the clear image sequence and the optimized optical flow matrix to obtain a target image sequence; and performing structure vibration displacement identification based on the target image sequence and the displacement proportion to obtain vibration displacement. According to the method, deblurring is carried out through a space-time coupling method, motion blurring is effectively eliminated, noise is suppressed, a clear image sequence is used for frame insertion, the frame density of the image sequence is increased, the problem of large inter-frame displacement caused by insufficient video frame rate is relieved, vibration recognition is carried out on the target image sequence in combination with the displacement proportion, and physical displacement of structural vibration is obtained. And the displacement identification precision under complex conditions is improved.

The invention provides a target object camera array imaging method based on multi-view-field deconvolution turbulence resistance, and relates to the technical field of image deblurring or deconvolution image processing based on a physical model, and the method comprises the steps: S1, building a physical model in which atmospheric turbulence affects the imaging quality of a target object, building a camera array imaging system, and carrying out the imaging of the target object; obtaining a target object image in a turbulence environment; s2, establishing a multi-view-field affine transformation registration model, and performing image registration through affine transformation to obtain a plurality of same-view-field image sequences of the target object; s3, performing deconvolution by using a convolution kernel of a point spread function of a preset optical system, enhancing the contrast of the target object image, and estimating the target object image; and S4, performing Fourier ring correlation operation based on the contrast-enhanced target object image, and performing adaptive iteration deconvolution again to obtain a recovered anti-turbulence target object image. According to the method, pre-deconvolution is performed on the original image, background noise is suppressed, and adaptive iteration deconvolution is performed after the image autocorrelation cumulant is calculated, so that anti-turbulence imaging is realized.

The invention discloses a dynamic scene deblurring method and system based on physical information adversarial learning, and the method comprises the steps: obtaining original blurred image data, carrying out the preprocessing of the original blurred image data, and obtaining a preliminary deblurred image; inputting the preliminary deblurred image into an initial dynamic scene deblurring model for training to obtain a dynamic scene deblurring model; the initial dynamic scene deblurring model comprises a generator network, an optical flow estimation network, a three-stage progressive training strategy and a multi-scale discriminator network, the generator network is used for mapping an initial deblurred image into a deblurred image, and the optical flow estimation network is used for estimating a motion field and calculating optical flow consistency loss; the three-stage progressive training strategy is used for carrying out three-stage training on the initial dynamic scene fuzzy model in sequence, and the multi-scale discriminator network is used for judging image authenticity on different scales; and inputting a to-be-processed blurred image into the dynamic scene deblurring model to obtain a blurred image. According to the invention, the deblurring effect is improved.

The invention belongs to the technical field of intelligent traffic, and particularly relates to an intelligent traffic snapshot method and system for real-time image optimization, and the system comprises an image collection module, an environment situation perception and prediction module, a dynamic parameter control module, an edge real-time image preprocessing and fusion module, and a semantic information extraction and quality evaluation module. The above modules work cooperatively through closed-loop feedback to realize multi-mode image high-speed synchronous capture, environment situation prediction based on deep learning, dynamic parameter control, edge real-time image optimization fusion and deblurring, and semantic-level image quality evaluation. Through adoption of the technical scheme, the image quality in a complex traffic environment can be remarkably improved, the real-time response capability is ensured, and the environmental adaptability is enhanced.

The invention relates to the technical field of machine vision, in particular to an intelligent sorting method and system for sundries on a conveying belt based on AI visual guidance, and the method comprises the steps: firstly, carrying out the deblurring of a to-be-detected image, eliminating the motion blurring, carrying out the image enhancement, enlarging the difference between the sundries and coal briquettes, and then achieving the precise recognition of the sundries through target detection, the impurity identification accuracy is improved from the source; then, image segmentation is carried out based on a sundry recognition result, then sundry geometric parameters and posture parameters are obtained, and an accurate target form basis is provided for grabbing; and finally, in combination with the speed of the conveying belt, the sundry detection position, the sundry maximum width, the sundry minimum width and the forward direction included angle, a motion path with synchronous time and adaptive posture is generated and converted into a control signal, a robot clamping jaw is controlled to clamp the sundry, and the problems of sundry recognition deviation, grabbing dislocation and response lag in a complex scene are effectively solved. The automatic sorting device is applied to automatic sorting of sundries on a conveying belt of a coal cleaning plant, and the sorting accuracy and efficiency can be improved.

The invention relates to the technical field of visual quality detection, in particular to a traffic engineering quality detection method and system based on machine vision, and the method comprises the following steps: calling a vehicle-mounted camera to collect a video stream and intercept a key frame, carrying out the motion deblurring of the key frame to generate a basic image, extracting high-frequency detail features through a first convolution kernel, and carrying out the recognition of the high-frequency detail features; extracting low-frequency structural features by using down-sampling and a second convolution kernel, splicing the low-frequency features after up-sampling the low-frequency features with the high-frequency features, calculating weights based on channel response intensity and generating multi-scale features, mapping the features into a defect probability matrix and calculating a dynamic threshold, screening regions with numerical values greater than the dynamic threshold as candidate connected domains, and extracting the candidate connected domains from the candidate connected domains; and extracting geometric parameters of the connected domain, comparing the geometric parameters with a standard disease form library, and determining disease categories. According to the method, the problem that fixed threshold segmentation cannot adapt to a complex environment is solved by eliminating motion blur, fusing high and low frequency features and combining dynamic threshold judgment and morphological parameter comparison, and the traffic facility disease detection precision and efficiency are greatly improved.

The invention provides a non-local information compensation Mama image deblurring method and system, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the feature extraction of a degraded image containing a fuzzy feature, and obtaining an initial shallow feature; taking the initial shallow features as input parameters, performing n times of first iteration processing, and then performing deblurring processing to obtain a first recovery feature map; the first iterative processing comprises deblurring processing and down-sampling; the deblurring processing is completed through an improved Mama module and an enhanced FFN module; performing n times of second iteration processing on the first recovery feature map, and then performing deblurring processing to obtain a second recovery feature map; the second iteration processing comprises deblurring processing and up-sampling; and performing convolution processing on the second recovery feature map to generate a residual feature map, and adding the residual feature map with the degraded image to obtain a recovery image. According to the scheme provided by the invention, high-quality restoration of the blurred image is realized on the premise of effectively controlling calculation and storage overhead.

This invention discloses a license plate recognition method and system for a parking lot patrol robot based on dynamic visual compensation, belonging to the field of data processing technology. The method includes: predicting the transient stationary point of chassis vibration based on motion posture data sequence to trigger license plate image acquisition; calculating motion blur vectors by combining angular velocity data and imaging intrinsic parameters to generate a spatial variation degradation kernel; using this kernel to perform non-uniform deblurring on the license plate candidate region, extracting and fusing the restored texture features and original topological features, and outputting the license plate recognition result and confidence level; calculating visual degradation parameters based on the blur vector length and confidence level, triggering deceleration and initiating hysteresis hold-up period control when the parameter exceeds a threshold. This invention solves the technical problems of non-uniform image motion blur caused by vibration and rotation in dynamic inspection and frequent control oscillations caused by single feedback, effectively improving the accuracy and operational stability of the robot's license plate recognition.

A method includes obtaining, using at least one under display camera, one or more first image frames associated with a first diffraction pattern and one or more second image frames associated with a second diffraction pattern. The first diffraction pattern and the second diffraction pattern are related through a transformation. The method also includes generating a first deblurred image using the one or more first image frames and a second deblurred image using the one or more second image frames. The method further includes combining the first and second deblurred images while exploiting complementary types of image artifacts created by the first and second diffraction patterns to generate an image of a scene.

The invention discloses a lightweight thermal infrared image enhancement and deblurring combined method, and aims to solve the problems of low contrast and fuzzy details caused by factors such as low environment temperature and obvious atmospheric thermal radiation effect in thermal infrared image capture. According to the method, joint optimization is realized through cascade design of a global enhancement encoder and a deblurring decoder, a global enhancement guide vector is extracted by using a space attention mechanism to perform feature domain enhancement, fuzzy features are corrected by predicting a translation mutability convolution kernel, original image detail expression is fused, a detail edge phenomenon is sharpened while textures are reserved, and the image quality is improved. And training the model by using a global contrast enhancement loss function and a detail recovery loss function. According to the method, two tasks of enhancement and deblurring are unified in an efficient and compact network framework, detail blurring is effectively inhibited while the contrast ratio of the whole image is improved, and the visual effect of the image is enhanced.

The application provides an image deblurring processing method, relates to the field of Internet of Vehicles and the field of artificial intelligence technology, and comprises the following steps: performing cascade coding processing on a first image based on M scales, and sequentially obtaining M scale coding images; performing cascade refining processing on the second scale coding image to the M scale coding image based on N scales, and sequentially obtaining N scale refining images; performing cascade decoding processing on the second scale coding image to the M scale coding image and the N scale refining image based on N scales, and sequentially obtaining N scale decoding images; and performing image prediction processing on the first scale coding image, the N scale decoding image in the N scale decoding image of the N scale decoding image, and the N scale refining image in the N scale refining image of the N scale refining image, so as to obtain a second image with higher definition than the first image. Through the application, the deblurring effect of multimedia images can be significantly improved.

This invention discloses an anti-motion blur video stream edge detection system, belonging to the field of computer vision technology. Utilizing motion blur analysis and adaptive filtering techniques, this invention accurately identifies and corrects motion blur in dynamic scenes, avoiding the destruction of edge details during deblurring as is common in traditional methods. It can generate clear and complete edge images in complex environments. Through real-time processing requirement analysis and dynamic optimization mechanisms, the system automatically adjusts filtering parameters and feature extraction complexity, ensuring high detection accuracy while meeting real-time requirements. This significantly improves the overall performance and practicality of the system, enabling it to perform excellently in applications with high real-time requirements, such as intelligent monitoring and autonomous driving, ensuring efficient and stable operation in real-world scenarios.

The application discloses a TDI-CCD image deartifact method and device, equipment, medium and product, and relates to the technical field of image processing. The method comprises the following steps: constructing a deep deblurring network; the deep deblurring network comprises a Wiener filter module and a generative adversarial network, and the Wiener filter module is arranged at the input end of a generator of the generative adversarial network; the Wiener filter module is used for filtering a frequency domain image; the deep deblurring network is trained, and the trained deep deblurring network is used as a deartifact model; during the training of the deep deblurring network, a point spread function and a regularization parameter of the Wiener filter module are used as learnable network parameters; and an image with row smear obtained from a TDI-CCD camera is processed by using the deartifact model to obtain a deartifact image. The application can effectively remove image artifacts and improve the definition of a TDI-CCD image.

The application discloses a real scene video deblurring system and method based on a single-step video diffusion model, comprising an encoding module, a denoising module and a decoding module, wherein the encoding module is used for respectively performing latent space encoding on each frame in a blurred video sequence to be recovered, generating a frame-by-frame latent space representation corresponding to the input video frame by frame; the denoising module is used for performing single-step denoising on the frame-by-frame latent space representation to obtain a latent space representation corresponding to a clear video; the decoding module is used for decoding the latent space representation corresponding to the clear video into image frames frame by frame and outputting according to the original time sequence of the input video to obtain a deblurred video result. Through frame-by-frame latent space encoding, frame-by-frame blur differences can be preserved, and through single-step diffusion distillation, reasoning delay can be reduced, and deblurring quality and reasoning efficiency are considered.