95 results about "Fuzzy graph" patented technology

The invention relates to the technical field of image optimization recognition, in particular to a deep foundation pit crack detection method and device for deep foundation pit tunneling. According to the method, a deep foundation pit image and a motion vector of an image acquisition device are obtained; and blurred images in the deep foundation pit images are screened out, and rigid edges are identified. And screening out the smear edge by using the position relationship between other edges and the rigid edge, the pixel value change stability on the path and the correlation degree between the edge vector and the motion vector. And constructing a two-dimensional fuzzy kernel, and adaptively setting a fuzzy kernel value by using gradient information and position information of pixel values in the two-dimensional fuzzy kernel. And performing deblurring on the blurred image through the two-dimensional blurring kernel, and performing crack identification. The feature information in the blurred image is extracted and analyzed, the smear effect in the image is quantified, the blurred kernel in the image is determined in a self-adaptive mode, deblurring is carried out, the quality of the deep foundation pit image is improved, and the accuracy of a crack recognition result is improved.

The invention discloses a fault diagnosis method and system based on a multi-source data association rule and a graph neural network. The method comprises the steps of extracting high-frequency operation data and low-frequency time sequence state data based on historical data, and establishing an equipment operation feature set; an Apriori algorithm is utilized to screen correlation characteristics to calculate a correlation relation, and a fault symptom set is constructed; and taking the association relationship of the features as an adjacent matrix embedded graph neural network, and training the constructed fuzzy graph neural network based on historical data to obtain a fault diagnosis model. The system comprises a data acquisition module, a preprocessing module, a feature extraction module, a feature screening module, a feature association relationship analysis module, a fault diagnosis model training module, a fault diagnosis module and a database storage module, and can perform multi-source data fusion analysis and training and updating of a fault diagnosis model. According to the method, the fault diagnosis model is constructed by combining multi-source data fusion, feature extraction, association relationship mining and the fuzzy graph neural network, so that more accurate and efficient fault diagnosis is realized.

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

The invention 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.

The invention discloses a myopia prevention and control display method and device, computer equipment and a storage medium, and the method comprises the steps: determining an object in front of a vehicle, the relative distance between which and the vehicle is greater than a first set distance, as a target watching object according to the environment information in front of the vehicle; a fuzzy image layer is displayed at the position, corresponding to the target watching object, of a target device so as to change the fuzzy degree of the target watching object, and the target device is a vehicle window of the vehicle or a head-mounted device connected with the vehicle; and if it is detected that the duration of watching the fuzzy layer by the user is greater than the set duration, cancelling the display of the fuzzy layer on the target device. Thus, when the duration of the user watching the target watching object in front of the vehicle is longer than the set duration, the target watching object is gradually cleared by canceling the display of the fuzzy layer on the target watching object, so that the ciliary muscles of the eyes of the user are helped to be gradually relaxed, and the user experience is improved. Therefore, the user can complete myopia prevention and control training in idle time in a vehicle driving or parking scene.

The invention relates to the field of track detection, in particular to a vision-based rapid identification method for a target object in a track area, which comprises a target object image sampling step, a fuzzy database construction step, a sampled image calibration step and a target object rapid identification step. The problems of image blurring, multi-source errors and real-time performance under high-speed movement are systematically solved; the beneficial effects of efficiently coping with image blurring and dynamic interference, improving system robustness and detection coverage rate through a multi-camera cooperative calibration mechanism, adapting to a complex operation environment through a dynamic error elimination strategy, and improving detection reliability and safety are achieved.

The invention discloses a least square improvement-based nearest projection point ambiguity resolution method. The method comprises the following steps of: constructing a relation model between ambiguity phase differences corresponding to various baselines of a double-long-baseline phase interferometer; grid division is carried out on the field angle of the double-long-baseline phase interferometer, and a phase difference fuzzy graph is constructed; expanding the relation model of the double-long-baseline phase interferometer to a multi-long-baseline interferometer, constructing a baseline vector, a theoretical fuzzy phase difference vector and a fuzzy number vector, and determining a hyperplane perpendicular to a phase line in the phase difference fuzzy graph; starting point fuzzy phase difference vectors of all phase lines and phase difference projection points of the phase lines on a hyperplane are determined based on the phase difference mode fuzzy graph; and performing least square fitting on an actually measured fuzzy phase difference vector corresponding to the actually measured multi-snapshot target signal, searching a fitted fuzzy phase difference vector with the minimum variance, determining the nearest phase difference projection point, and obtaining a corresponding fuzzy number vector to realize ambiguity resolution.

The invention relates to the technical field of image processing, and relates to an inspection fuzzy image recognition method and device based on a region target detection algorithm. The method comprises the following steps: acquiring an original image shot by a to-be-identified device; carrying out image preprocessing, and inputting the image into the trained region target detection network for processing; and carrying out deblurring processing by adopting a multi-Wiener filtering algorithm to obtain a reconstructed clear image, and finally carrying out image recognition. According to the invention, a region target detection network based on YOLO network modification is adopted, so that the to-be-deblurred target region in the equipment image can be accurately identified while the model is lightened; according to the method, a multi-Wiener filtering algorithm is adopted, so that the to-be-deblurred target area in the equipment image can be effectively deblurred; according to the method, the clear image can still be generated under the condition that the inspection robot moves, the definition and detail performance of the image are greatly improved, the recognition accuracy is further improved, and misjudgment caused by blurring is reduced.

The invention relates to the technical field of blurring detection, and discloses a motion blurring detection method and system based on deep learning and blind deconvolution, and the method comprises the steps: collecting an original image, and carrying out the sub-region division of the image; singular value decomposition is carried out on each sub-region; performing fuzzy region detection on the sub-regions according to a decomposition result; performing image restoration on the blurred image; and segmenting target contour information in the fuzzy restored image by using a deep learning method, and outputting a clear image with a target contour. By introducing the SPD-Conv, the improved YOLOv8 model can more accurately segment a low-resolution fuzzy target, and the segmentation precision is effectively improved. And the method can adapt to low-resolution fuzzy target segmentation tasks in different scenes. While the segmentation precision is improved, the calculation complexity and the reasoning time of the model are not remarkably increased, the real-time advantage of the YOLOv8 can still be kept, and the requirement for real-time performance in practical application is met.

The invention belongs to the technical field of SLAM (Simultaneous Localization and Mapping), and discloses a robot vision algorithm for a large visual angle, a storage medium and equipment, and the algorithm comprises the steps: S1, collecting image information, extracting feature information from an image through employing a multi-scale and multi-stage neural network, and repairing a fuzzy region; s2, performing position prediction on the feature points matched with the image based on an IMU sensor, and judging whether the feature points of the next frame are in a prediction range or not; s3, dividing the image into a plurality of regions, calculating the information entropy of each region, and performing grid division according to the size of the information entropy; and S4, providing a new neighbor grid domain grid statistical algorithm to further screen the feature points screened by the IMU sensor. According to the method, the blurred image frame is repaired, so that a large number of image feature points can be accurately extracted in large-view-angle motion, and timeliness and accuracy are improved in accurate matching through improvement.

The present invention discloses a visual recognition method for bolt missing in a pipe gallery under the condition of robot mobile shooting. The method corrects the red channel of the bolt missing visual recognition data set, uses a median filter and a DnCNN combined model to remove impulse noise and Gaussian noise, and on this basis, performs grayscale transformation calculation, cepstrum compression operation and centering operation on the blurred image, performs edge detection operation and Radon transformation on the obtained image, and correctly estimates the blur angle; calculates the peak value and blur angle by utilizing the peak symmetry of the first-order differential autocorrelation and the cepstrum three-dimensional graph; constructs a point spread function for the blur parameter, and uses the Richason-Lucy algorithm to iterate the blurred image for multiple times, thereby restoring the image. The training set and the validation set are input into the improved Yolov8 model to prove the effectiveness of the constructed model. The present invention can achieve the purpose of improving calculation efficiency and prediction accuracy.

The invention discloses a toothbrush defect detection method and a sorting device, and belongs to the technical field of image processing, and the method comprises the following steps: establishing a feature space containing four-dimensional core parameters, forming a dynamic feature database, setting a difference identification method, constructing a basic defect model, monitoring the change condition in the production process in real time, and finely adjusting the basic defect model, the feature change is adapted; the method comprises the following steps: acquiring an original image sequence of a to-be-detected toothbrush, setting a blurring restoration method, and performing sharpening processing on a blurred image, thereby solving the problem of defect missing detection caused by image blurring in a dynamic shooting process, determining a key detection area of the to-be-detected toothbrush, facilitating accurate positioning of a defect area, reducing missing detection risk, setting a defect detection method, and improving the detection accuracy of the to-be-detected toothbrush. And performing defect detection on the key detection area by utilizing the finely-adjusted defect model, and grading the toothbrush to be detected by combining a grading rule.

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 relates to an implicit diffusion super-resolution assisted remote sensing image small target detection and identification method. The method comprises the following steps: acquiring a training data set; obtaining a clear-fuzzy sample pair; and introducing the target classification model and the implicit diffusion model into the target detection and recognition model to obtain a small target detection and recognition model. The recognition method is used for effectively supplementing feature information when small target detection and recognition are carried out, so that the accuracy of small target detection and recognition is improved. According to the invention, aiming at a difficult point with less effective information in small target detection and identification, a low-resolution blurred image is gradually recovered to a high-resolution clear image by means of an implicit diffusion model based on deep learning, super-division of an image area where a small target is located is realized, and the definition of the area image is improved, so that feature information is enriched, and the detection and identification efficiency is improved. The problems of insufficient feature information, unclear target boundary, susceptibility to noise interference and the like in small target detection and recognition are relieved, and the overall effect of remote sensing image target detection and recognition is finally improved.

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 discloses a fuzzy type adaptive image restoration method, which belongs to the field of image processing and computer vision, and comprises the following steps: carrying out frame extraction processing on an input video stream, and preprocessing extracted image frames; based on the preprocessed image, a comprehensive fuzzy score is calculated through a multi-feature fuzzy evaluation mechanism; judging whether the image frame is a fuzzy frame according to the comprehensive fuzzy score; if the image frame is a fuzzy frame, judging the fuzzy type of the image frame through a preset rule according to a plurality of evaluation scores in a multi-feature fuzzy evaluation mechanism; selecting a corresponding restoration strategy from a plurality of preset restoration strategies to restore the image frame, and generating a restored image; performing quality evaluation on the repaired image, and if an evaluation result does not meet a preset condition, adjusting a repairing strategy and repairing again; and re-inserting the repaired image meeting the preset condition into the input video stream, and outputting the repaired video. According to the invention, efficient identification and accurate restoration of the blurred image frame are realized.

According to the high-speed fuzzy license plate character recognition method based on deep learning, a deep convolutional generative adversarial network BLPDCGAN generates a high-quality fuzzy image similar to a real fuzzy license plate, a data set is expanded, and the model generalization ability is improved. The generation module introduces a noise image to enhance the blurring effect, and optimizes the quality of the generated blurred image through MSE loss and adversarial loss. The blurred license plate character recognition module uses a Unet architecture for reference, integrates a CBAM attention mechanism and multi-scale feature fusion, enables the model to pay more attention to character features rather than noise, and improves the feature extraction capability of blurred images. The feature enhancement module further enhances the structure and texture extraction of the fuzzy characters, and increases the robustness and recognition precision of the model to the characters in the fuzzy scene. According to the multi-scale feature fusion strategy, features with different resolutions are aggregated, so that the model can capture key feature details of a fuzzy region and optimize the overall recognition effect.

The application discloses a kind of unmanned aerial vehicle image enhancement method and system.Therein, the method includes: obtaining first fuzzy image and its corresponding semantic prompt text;The structural feature extraction is carried out to first fuzzy image, and the semantic feature extraction is carried out to semantic prompt text, to obtain prior information;Prior information is injected into denoising model using cross attention mechanism;First fuzzy image is input into the denoising model injected with prior information to carry out multiple rounds of training and obtain target denoising model;Second fuzzy image is obtained, which is subjected to frequency domain transformation and input into frequency domain residual error model for multiple rounds of training to obtain target frequency domain residual error model;The fuzzy image to be detected is input into target denoising model for denoising to obtain spatial clear image;The fuzzy image to be detected is input into target frequency domain residual error model for prediction to obtain frequency domain clear image;Spatial clear image and frequency domain clear image are fused to obtain high-resolution image.The method can obtain higher quality clear image.

The invention relates to the technical field of three-dimensional reconstruction, and discloses a three-dimensional scene reconstruction method and device, equipment, a storage medium and a program product, and the method comprises the steps: carrying out the initialization of a three-dimensional Gaussian ellipsoid based on an obtained fuzzy image sequence and an event sequence, and obtaining an initial Gaussian ellipsoid; constructing a loss function based on a clear picture sequence obtained by rendering the fuzzy picture sequence through the initial Gaussian ellipsoid and geometric constraints of an event-ungenerated area corresponding to the fuzzy picture sequence and the event sequence; taking the minimum value of the loss function as an optimization target, and adjusting the parameter information of the initial Gaussian ellipsoid to obtain a target Gaussian ellipsoid; and performing three-dimensional scene reconstruction based on the target Gaussian ellipsoid to obtain a target three-dimensional scene. Therefore, three-dimensional reconstruction is carried out in combination with a fuzzy picture sequence obtained by a traditional camera and an event sequence obtained by an event camera, the reconstruction effect of a three-dimensional scene is improved, geometric constraint is added to guide and adjust a Gaussian ellipsoid, and the reconstruction effect of the three-dimensional scene is further improved.

The application belongs to the technical field of simultaneous localization and mapping, and particularly relates to a SLAM algorithm based on feature reinforcement and motion judgment in a dynamic scene, which applies a feature reinforcement instance segmentation network FENET and comprises the following steps: step S1, collecting image information and realizing feature recovery of a dynamic fuzzy object through a fuzzy feature recovery module; step S2, guiding a model to focus on key features of an object based on a reinforced feature recognition mechanism, and recognizing potential dynamic objects; and step S3, jointly estimating the pose of a camera itself and judging the motion of an object to remove a dynamic object. The application can reconstruct and recover lost feature information from a fuzzy image, greatly improves the recognition accuracy of a system for a dynamic object, greatly improves the recognition accuracy of a dynamic object, and avoids misjudgment of static features.

The present application provides a blur processing method and device, wherein the blur processing method includes: obtaining an initial image; creating a reduced image of a corresponding blur level based on the initial image; determining a target image in the reduced image and determining a sampling offset of the target image in the pixel dimension based on preset blur information and pixel information of the initial image data; blurring the target image based on the sampling offset to generate a blurred image corresponding to the initial image.

The invention discloses a small sample hyperspectral image classification method and device based on fuzzy contrast graph convolution. The method comprises the following steps: determining membership information, fuzzy node initial feature vectors and similarity of each initial graph node corresponding to each fuzzy cluster based on a hyperspectral image; generating a fusion weight, a fusion feature and a target feature according to convolution features output by convolution of different layers of fuzzy images; performing linear transformation operation and probability distribution operation on the target features in sequence to obtain the probability that the corresponding initialized graph node belongs to each category; and performing argmax operation on the probability to obtain a category index corresponding to the hyperspectral image. The uncertainty of pixels is effectively processed through fuzzy learning, interference of neighborhood pixels is weakened, the uncertainty between the pixels is better processed, and meanwhile richer and more expressive feature representation is obtained. The method has higher classification effect and robustness, and plays an important role in multiple fields of mineral exploration, environment monitoring, forest management, precision agriculture and the like.

The present invention discloses a method and system for ultrasonic logging image blind restoration based on fuzzy factor estimation variation, which estimates the radius of the fuzzy factor by analyzing the logarithmic spectrum of the ultrasonic logging fuzzy image, thereby improving the stability of the subsequent iterative optimization algorithm, and at the same time, by constructing a variational blind restoration model based on the L1 norm, fully utilizing the sparse characteristics of the ultrasonic logging image, effectively improving the quality of the restored image. Compared with the existing ultrasonic logging image blind restoration method, it can effectively improve the clarity and contrast of the ultrasonic logging image, provide high-quality images for subsequent logging exploration, and has very good practicality.

The invention relates to the technical field of image processing, computer vision and image blind deblurring, in particular to a self-supervised image blind deblurring method based on hierarchical Bayesian representation, which comprises the following steps: acquiring and processing a clear image, synthesizing a blurred image, and establishing a blurred image data set; constructing a hierarchical Bayesian prior model and a target function based on the motion degradation model in combination with the blurred image data set, and defining a random variable; the objective function is minimized, and posterior distribution of random variables in the layered Bayesian prior model is solved; a deep neural network framework is designed, and a loss function is established and trained in combination with posterior distribution of random variables to obtain an optimal model; outputting an image recovery result of the blurred image data set through the optimal model; according to the method, sparse adaptation optimization is realized on the basis of the hierarchical Bayesian prior model, the problems of insufficient generalization and poor robustness of a VB-based blind deblurring algorithm are solved, and the method can be applied to high-precision demand scenes such as image enhancement.

The invention discloses an intelligent picture data cleaning and dividing method and device and a medium, and the method comprises the steps: detecting the feature points of each picture one by one through a deep learning model, reading the number of the feature points of each picture, and removing the pictures of which the number of the feature points is smaller than a preset value; carrying out data set division on the pictures left after elimination, and carrying out feature point matching on the remaining pictures one by one by using another deep learning model; and splitting the two pictures of which the matching degree is greater than a threshold value into a training data set and an evaluation data set according to a matching result. According to the method, the fuzzy pictures in the picture data can be effectively eliminated through deep learning, and the pictures with high similarity can be divided into the training data set and the evaluation data set, so that the neural network model can learn richer features, the picture data processing efficiency is high, the utilization rate of the picture data is high, and the waste of the picture data is reduced.

The invention relates to the technical field of data management, and discloses an enterprise sensitive data desensitization method and system based on a natural language, and the method comprises the steps: carrying out the classification processing of original enterprise data, obtaining classified enterprise data, carrying out the character feature extraction of enterprise text data, obtaining text feature characters, and carrying out the desensitization of enterprise sensitive data; evaluating character utility degrees corresponding to the text feature characters; marking desensitized feature characters in the text feature characters, and performing semantic fuzzification processing on the desensitized feature characters to obtain fuzzy feature characters; performing area blurring processing on the image desensitization area to obtain a blurred image area; desensitizing the enterprise numerical data to obtain target numerical data; and performing data updating processing on the classified enterprise data by using the target numerical data, the fuzzy image region and the fuzzy feature characters to obtain enterprise desensitization data corresponding to the original enterprise data. The main purpose of the invention is to solve the problem of low flexibility of enterprise sensitive data desensitization.

The invention relates to the technical field of image display, in particular to a method and device for generating a fuzzy layer of an auxiliary screen, a display screen and a vehicle. The screen is divided into a main screen and an auxiliary screen, the auxiliary screen is simulated through a virtual screen, and the auxiliary screen covers display content, located in an auxiliary screen display area, of the main screen; under the condition that a first image layer displayed on the auxiliary screen comprises a fuzzy image layer, the method comprises the steps that a second image layer which is located below the fuzzy image layer and has an intersection with the fuzzy image layer is determined, and the second image layer is an image layer on the main screen; under the condition that the second image layer meets a first condition, performing fuzzy processing based on rendering information of an application corresponding to the second image layer to generate a fuzzy image layer; and under the condition that the second image layer meets a second condition, obtaining a snapshot of the main screen, and performing fuzzy processing on the snapshot to generate the fuzzy image layer.

The present invention discloses an image-driven tick detection comb that integrates a deep learning model, which relates to the field of tick detection technology and includes an initial exposure time configuration module, an image data acquisition module, a feature extraction and fuzzy quantization module, a fuzziness prediction module, an image classification module, a normal image processing module, and a fuzzy image exposure adjustment module; the initial exposure time configuration module first determines the initial exposure time that is adapted to the current environment and detection requirements, and performs parameter configuration on the micro camera. The present invention uses deep learning and image processing technology to accurately predict image fuzziness, distinguish between normal and fuzzy images, and adopts adaptive exposure time control for fuzzy images to reduce motion blur, ensure image clarity, improve feature extraction accuracy, reduce the probability of misjudgment, detect and remove ticks in a timely manner, and reduce the risk of host infection with pathogens. Dynamic exposure adjustment enhances the robustness and reliability of the equipment in different environments, significantly improving detection accuracy.

The invention discloses a fuzzy robust facial expression recognition method based on a frequency domain convolutional neural network, and the method comprises the steps: 1, calculating a frequency spectrum matrix difference value of a clear image and a fuzzy image through Fourier transform, and quantizing the influence of fuzzy on different frequency components, the smaller the frequency spectrum difference value is; 2, designing a self-adaptive fuzzy weight layer based on the frequency spectrum difference, taking a 48 * 48 frequency spectrum image as the input of the layer, and carrying out convolution with different filters; and step 3, designing a fuzzy robust frequency convolutional neural network, adding ABWL in the convolutional neural network to process the input spectrum image and highlight a fuzzy insensitive feature region, then extracting features of the processed image through CNN, and finally completing classification through a full connection layer. According to the method, the frequency components with relatively small fuzzy influence are separated by analyzing the spectrum characteristics of the image, so that the recognition capability of the network on the blurred image is well improved.

The present disclosure relates to an image processing method and device, an electronic device, and a storage medium. The method includes: obtaining the depth of field data of an input image, and obtaining the weight matrix and weighted image of the input image based on the depth of field data; performing lateral integration on the weight matrix and the weighted image respectively to obtain a first lateral integral map corresponding to the weight matrix and a second lateral integral map corresponding to the weighted image; based on the above two lateral integral maps, respectively obtaining the fuzzy values ​​corresponding to each pixel in the weight matrix to obtain a weighted fuzzy image corresponding to the weight matrix and a weighted fuzzy image corresponding to the weighted image; obtaining a target image based on the weighted fuzzy image and the weighted fuzzy image. The embodiment of the present disclosure obtains a fuzzy kernel suitable for any shape by obtaining a lateral integral map to obtain a fuzzy value, which can improve the scope of application.