313 results about "Computer image" patented technology

The invention relates to an infrared and visible light image fusion method based on a cross-domain Transform, and belongs to the field of computer image processing. The method comprises the following steps: respectively carrying out preprocessing operation on an infrared image and a visible light image to obtain a training data set; an end-to-end image generator network is designed, an encoder module is used for extracting deep semantic features of an infrared image and a visible light image, a fusion module introduces an axial attention mechanism to enhance the global modeling capability of the features, and feature fusion is carried out in combination with information of a spatial domain and a frequency domain; the fused features are gradually recovered to an image space through a decoder module, and a fused image is generated; constructing a fusion loss function module, and guiding the network to focus a significant feature difference between the source image and the fusion image based on a comparative learning idea; and finally, inputting the infrared and visible light image Y channel into the network model, generating a fusion image, completing a training process, and realizing unified optimization of fusion performance and visual quality.

The invention provides a processing method and system for reconstructing an LDI sub-pixel image through sparse compression and application, and the method comprises the steps: calculating a low-resolution to-be-exposed image corresponding to each phase structure in advance through a computer image compression algorithm; the low-resolution to-be-exposed images obtained through calculation are loaded to the digital micro-reflector according to the time sequence, and the display moment of each pattern is matched with the rotation position of the corresponding phase structure; coupling the pattern of the digital micro-mirror and the phase encoding mask in a frequency domain through a 4-f optical system, and reconstructing a high-resolution exposure pattern at a sub-pixel level by utilizing a diffraction effect; projecting a high-resolution light field of the reconstructed high-resolution exposure pattern to the surface of the photoresist, and forming a target circuit pattern by accumulating exposure dose; and the micro-nano structure with sub-pixel precision is obtained after development. The system comprises an image compression module, a pattern matching module and a pattern forming module. According to the invention, the manufacturing cost of the laser direct imaging equipment is reduced under the condition of the same precision.

The invention discloses a virtual fitting method and device, a storage medium and electronic equipment, and is applied to the technical field of computer image processing, and the method comprises the steps: processing a target video, and obtaining the human body posture data of each video frame in the target video; constructing a 3D human body model of each video frame by using the human body posture data of each video frame; for the 3D human body model of each video frame, acquiring clothing rendering information of the 3D human body model, and rendering virtual clothing on the 3D human body model based on a dynamic fitting algorithm and the clothing rendering information to obtain a virtual rendering video frame of the video frame; and generating an AR fitting video based on each virtual rendering video frame, and displaying the AR fitting video to the user. Therefore, the selected costume can be tried on in the AR form, the fitting effect of the costume can be displayed for the user, the problem that the size is improper due to the fact that the costume purchased online cannot be tried on is solved, the probability of refunding and changing goods is reduced, and good shopping experience is provided for the user.

The invention discloses an industrial surface defect image classification method based on hybrid query strategy active learning, and belongs to the technical field of computer image processing and machine learning. The invention aims to solve the technical problems of high cost, long period and low rare defect recognition rate caused by category imbalance due to dependence on large-scale manual labeling. The core of the method is to execute a hybrid query strategy in an iterative loop: firstly, screening out a candidate sample set of model cognitive ambiguity through uncertainty measurement of prediction entropy; secondly, in the candidate set, a core set and hierarchical thought diversity sampling method is adopted to select a final to-be-labeled sample with both characteristic representativeness and category balance. And the query batch is manually annotated and then is used for carrying out iterative updating and optimization on the model. According to the method, the recognition precision and generalization ability of the classification model on various defects can be remarkably improved with extremely low manual labeling cost, and the model development period is greatly shortened.

The invention discloses a ginseng seed lossless classification method and a ginseng seed lossless classification system, relates to the technical field of computer image detection, and solves the problem that in the prior art, a ginseng seed classification method based on image and spectral characteristics of ginseng seeds is lacked. Respectively collecting image data of the ginseng seeds and hyperspectral data of the ginseng seeds; respectively preprocessing the image data of the ginseng seeds and the hyperspectral data of the ginseng seeds; respectively carrying out feature screening on the preprocessed image data of the ginseng seeds and the preprocessed hyperspectral data of the ginseng seeds; fusing the image data of the ginseng seeds after feature screening and the hyperspectral data of the ginseng seeds; after the RBMO algorithm is improved, the RBMO algorithm is combined with the RF model to construct an ORBMO-RF model; and respectively inputting the fused image data of the ginseng seeds and the fused hyperspectral data of the ginseng seeds into an ORBMO-RF model for processing, thereby completing classification of the ginseng seeds.

The invention discloses an interactive image segmentation algorithm based on an algorithm computer, and belongs to the technical field of computer image segmentation. The interactive image segmentation algorithm comprises the following steps: S1, image uploading and preprocessing, S2, construction of a bidirectional interactive propagation mechanism, S3, adaptive image segmentation, and S4, image detection after segmentation. The interactive image segmentation algorithm is combined with lightweight semantic propagation, adaptive segmentation and a user feedback closed loop, the user interaction frequency is remarkably reduced while the precision is ensured, and compared with a pure deep learning scheme, the interactive image segmentation algorithm is more suitable for small sample scenes and is suitable for scenes with double requirements on precision and efficiency, such as medical images and industrial quality inspection; the algorithm can quickly adapt to a new field, a new category or an unknown object, reduces dependence on a large amount of training data in a specific field, minimizes user interaction times and complexity on the premise of ensuring high segmentation precision, can improve user experience, and has the characteristics of extremely simple interaction, ultrahigh precision, superstrong robustness and real-time response.

The invention discloses an unmanned aerial vehicle small target detection method based on feature enhancement and selective attention and a related device, and relates to the technical field of computer image target detection, and the method comprises the steps: obtaining an aerial image of an unmanned aerial vehicle, adjusting the image to a preset resolution, and obtaining an adjusted image; inputting the image into a backbone network of a network architecture, extracting a multi-level feature representation through a plurality of CALBlock feature extraction modules, and enhancing features of the multi-level feature representation through an EMIT edge enhancement architecture to obtain an enhanced multi-level feature representation; inputting the enhanced multi-level feature representation into a neck network of the network architecture, and performing cross-scale feature fusion and enhancement through an FSAFPN structure to obtain a multi-scale enhanced feature map; and inputting the multi-scale enhanced feature maps into a detection network of a network architecture, and performing target detection by using a decoder to obtain target category probability distribution and bounding box coordinates.

The present invention relates to the technical field of computer image processing, and in particular to an agent-based image analysis and processing method and system, and a storage medium. Image quality analysis and estimation is performed by means of a pre-trained image quality analysis model, and a corresponding processing strategy and a corresponding image processing sequence are generated, thereby achieving automatic image quality analysis and evaluation and facilitating selection of different processing strategies based on different image content, so as to satisfy processing requirements of various application scenarios. A corresponding image processing model is retrieved on the basis of the result of matching between the processing strategy and basic information of each image processing model in a knowledge base, and then called according to the image processing sequence, and an automatic image processing flow control model automatically controls the image processing model to execute image processing according to the image processing sequence and performs monitoring, thereby achieving automatic image processing, reducing the requirement of manual intervention, and saving the time and cost.

The invention provides an efficient image super-resolution method and device based on dual guidance semantic enhancement, and relates to the technical field of image super-resolution, visual content generation and the like in computer image process.The method comprises the steps that an image super-resolution model containing a pre-training diffusion model backbone structure is constructed, and a pre-training diffusion model is constructed; a trainable LoRA module is inserted into a U-Net structure and a VAE encoder of the image super-resolution model; calculating the perception image loss of the generated image and the real image by adopting a dual-guidance quality enhancement training strategy; based on a semantic alignment method of score matching, performing semantic feature constraint on the generated image through a condition vector of a pre-training diffusion model to form semantic consistency loss aligned with real image distribution; and carrying out end-to-end training on the image super-resolution model under joint optimization of perceptual image loss and semantic consistency loss, and after the training is finished, realizing high-resolution generation of a low-resolution image only through one-time U-Net reasoning.

The invention relates to the technical field of computer image processing, in particular to a farmland forest network extraction method and system based on a SegFormer model, and the method comprises the steps: obtaining a remote sensing image of a farmland forest network; the remote sensing image is preprocessed; performing image annotation on the preprocessed remote sensing image to obtain a training data set; a farmland forest network extraction model based on a Segform model is constructed; adopting the training data set to train the farmland forest network extraction model; and inputting a remote sensing image of a to-be-detected area into the trained farmland forest network extraction model to obtain a high-precision farmland forest network target area. According to the embodiment of the invention, the SegFormer model is optimized, the jump connection module is added, and the CBAM module is introduced, so that the perception capability of the model for space and channel information is enhanced, the segmentation precision is further improved through the jump connection and the up-sampling module, and the farmland forest network can be accurately extracted.

The invention belongs to the field of tunnel wall surface deformation monitoring, and particularly relates to a tunnel wall surface deformation monitoring method based on computer image recognition, which comprises the following steps: acquiring expandable data and accessing the expandable data to a subsystem; image stabilization operation is carried out on the edge side, and a standardized preprocessing subsystem is constructed to process data; constructing a multi-modal fusion architecture of a structural topological graph and a space-time diagram neural network; constructing a hierarchical alarm judgment mechanism of an uncertainty-driven risk score and a sectionalized adaptive threshold, and linking the BIM and work order circulation to form a closed loop; according to the invention, millimeter-level displacement resolution can be realized, false alarm and missing alarm can be obviously reduced, and long-term online traceable tunnel wall surface deformation monitoring and graded early warning closed loop can be supported.

The invention relates to a real scene image defogging method based on disturbance defense and semantic guidance, and belongs to the field of computer image processing. The method comprises the steps that a disturbance defense module is constructed, defended features are obtained and input into a defogging network, feature extraction is conducted on the defended features in the forward propagation process, the defended features are input into a defogging decoder, and low-dimensional features are further recovered into defogged images; inputting the composite domain foggy image into a source defogging network to process and output an intermediate feature; the features extracted by the feature extraction module are input into a semantic decoder; the semantic decoder uses the learned semantic features to help the image restoration of the defogging network; the defogging features of the defogging decoder and the semantic features of the semantic decoder are fused; training a content disturbance defense module, a semantic decoder, a source defogging network, a multi-scale fusion module and a defogging network; and the trained defogging network is used for defogging the foggy image. According to the invention, a clearer and more real defogged image can be generated.

The invention discloses an agricultural greenhouse water and fertilizer management system and method based on video recognition, and relates to the technical field of computer image recognition, and the system comprises a camera which is used for collecting crop images; the environment sensor is used for collecting water and fertilizer environment data of the agricultural greenhouse; the local host is provided with a crop identification model, and the crop identification model is used for identifying the real-time growth state of crops in the crop image; the cloud server is deployed with a growth decision model and a growth regulation model, the growth decision model formulates a growth control strategy according to the types and growth stages of crops in the agricultural greenhouse and the water and fertilizer environment data, the growth regulation model calculates an income value, and a regulation scheme for the growth control strategy is solved by taking the maximum income value as a target; and forming an adjusted growth control strategy. According to the method, the growth state of the crops is considered on the whole instead of achieving the optimal state of some crops, and the benefit of the agricultural greenhouse is improved.

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

The invention relates to the technical field of computer image processing and medical image analysis, in particular to a cervical cancer MRI (Magnetic Resonance Imaging) image segmentation method for improving a U-Net structure based on a feed-forward channel double-attention mechanism. In order to solve the problems that a traditional U-Net structure is weak in multi-scale information extraction capability, inaccurate in boundary fuzzy region recognition and the like in processing female abdominal cervical cancer MRI images, the method proposes that a feedforward connection mechanism and a double-attention mechanism are embedded into an encoder and a bottleneck module to form a novel U-Net segmentation model; the recognition and segmentation precision of the cervical cancer focus area is improved, and a high-quality image basis is provided for subsequent clinical diagnosis and treatment. A double-attention mechanism is integrated into a plurality of key nodes of the model, a joint channel-space attention module is added to the tail of each convolution module in an encoder, a cavity space pyramid pooling structure is introduced to a bottleneck position, a channel attention mechanism is embedded, and joint modeling of cross-scale, multi-channel and space context information is achieved.

The invention discloses a large-scale rotary equipment blade three-dimensional profile measurement and point cloud splicing method and system based on line laser, and belongs to the technical field of precision measurement and computer image processing. Reading a CAD model of a to-be-measured blade, extracting geometric features of the blade by a system, and analyzing the geometric features to generate a plurality of measurement path candidate sets; a measured blade is fixed on a rotary table, a multi-dimensional measurement movement mechanism is adopted to carry a line laser sensor to scan the surface of the blade step by step according to a planned final path, and accurate three-dimensional point cloud data is obtained; the method comprises the following steps: after acquiring point cloud data of a blade, initially aligning the scanned point cloud data in combination with position information of equipment; the optimized ICP algorithm is used, a transformation matrix between the point clouds is continuously adjusted through an iteration mode, the distance between the point clouds is minimized, mismatching and noise influences are further reduced, and therefore accurate registration is achieved. The invention aims to solve the problems of low precision and poor efficiency of aero-engine blade three-dimensional profile measurement.

The invention relates to a controllable Chinese character new font generation method and system based on a diffusion model, and belongs to the technical field of computer image processing. Based on the system, a user inputs a character image of a target stroke style and a character image of a target skeleton, then multi-scale skeleton features are extracted through a skeleton information extraction network, stroke style features are extracted through a stroke style extraction network, and the multi-scale skeleton features are extracted. And finally, denoising operation prediction of each step of the U-net network in the diffusion process is guided through the features, so that a new font character image conforming to expectation is generated. The method is inspired by a structure reference method, features are extracted from the input image through the neural network, a diffusion model is guided through skeleton features and stroke features to generate a new font style, and basic requirements of users are met.

The invention discloses a cast-in-place pile pouring process quality detection method capable of sensing ERT on the inner side and the outer side. The cast-in-place pile pouring process quality detection method comprises the steps that conductivity information of the inner side and the outer side of a cast-in-place pile is collected through an electrical resistance tomography electrode array arranged at the bottom of a cast-in-place pile feeding guide pipe; the two-dimensional position information of the electrical resistance tomography electrode array in the pile hole in the horizontal direction is positioned through an ultrasonic transducer array arranged at the bottom of the feeding guide pipe; the depth information of the electrical resistance tomography electrode array is acquired through an incremental encoder and a roller which move along with the feeding guide pipe in the vertical direction; transmitting the obtained conductivity information, the two-dimensional position information and the depth information to a computer imaging system to obtain a three-dimensional conductivity distribution image of the cast-in-place pile, and analyzing the obtained image to realize quality detection of the cast-in-place pile. Total cross-section imaging of concrete and a reinforcement cage can be achieved, and the positions and sizes of karst caves and collapse possibly existing in the pile hole wall can be accurately measured.

The invention belongs to the technical field of computer images, and provides an object counting method and device for enhancing text guidance by utilizing frequency characteristics. The method comprises the following steps: inputting a query image to a visual encoder to obtain visual features; inputting the object description text to a text encoder to obtain text embedding; fusing the visual features and the text features by using a fusion network to obtain fusion features; the fusion features are input into a decoder to obtain a density estimation graph of the object, the decoder comprises K cascaded adaptive frequency selection modules and one output layer, and the kth adaptive frequency selection module comprises a first convolution unit, an adaptive frequency selector and a first up-sampling unit; the kth self-adaptive frequency selector carries out filtering processing on the amplitude spectrum and the phase spectrum of the output characteristic of the first convolution unit in the frequency domain, and converts a filtering processing result into a spatial domain; according to the method, related frequency components are dynamically emphasized in decoding, the space domain and frequency domain features are combined, and accurate object positioning and accurate counting are achieved.

The invention discloses a method and a system for detecting fan blade defects based on computer vision. In computer image visual inspection of the fan blade, the shooting visual angle of a camera and the fan blade are of a three-dimensional structure with curvature, so that the shot image of the fan blade is distorted and deformed, and the defect identification precision is influenced. Therefore, by adopting a Mosaic data enhancement technology, the shot image of the fan blade is corrected according to the curvature of the fan blade, so that the analysis precision of subsequent blade defects is improved. In addition, an attention module CBAM is introduced, so that the detection efficiency of the visual image of the blade is improved; by improving the three-dimensional defect reconstruction method, the three-dimensional defect reconstruction precision of the fan blade is further improved; by subsequently introducing laser detection equipment and ultrasonic detection equipment, the accuracy of blade defect detection is further improved by adopting a fusion judgment method.

The invention discloses an image fusion method based on iterative double-branch attention and modal difference guidance, and belongs to the technical field of computer image vision. In a feature extraction stage, a CNN-based shallow feature extraction module is used to obtain shallow features, and the output of the features has two branches: one branch continues to be input to a Transform-based depth feature extraction module to extract depth features; the other branch is input to the fusion module to realize effective utilization of multi-stage features; in the feature fusion stage, according to the deep and shallow features obtained in the feature extraction stage, firstly, an attention module capable of iterating double branches is utilized in the fusion stage to capture detail information of the extracted shallow and deep features of the infrared and visible light images, and more important feature information is reserved; and then, the reserved important detail information is input into a fusion module based on modal difference guidance of Transform, and the fusion module can reserve difference information of different modals.

The invention discloses a garden disease and pest identification method and system based on computer image processing, and belongs to the technical field of disease and pest monitoring, and the method specifically comprises the steps: collecting a visible light image, an infrared image and environment parameters of vegetation; performing space-time registration on the visible light image and the infrared image and generating a three-dimensional temperature field model; the three-dimensional space is divided into grid units, and the temperature statistical characteristics and the vertical direction temperature gradient of each unit are extracted; obtaining an expected temperature gradient direction through a preset standard three-dimensional radiation field reference model; and when the actual gradient direction is consistent with the expected gradient direction, calculating a gradient direction deviation value of each grid unit, calibrating the grid unit with the deviation exceeding a threshold value as an abnormal region, and extracting a corresponding visible light image to carry out pest recognition. According to the invention, early-stage accurate identification of diseases and insect pests is realized, systematic diseases and local insect pests are effectively distinguished, interference of environmental factors is overcome, and the reliability of insect pest monitoring is improved.

The embodiment of the invention discloses an image rendering display method and device, equipment, a medium and a product, and relates to the technical field of computer image processing. The method comprises the following steps: rendering a scene where a three-dimensional model object is located to obtain a scene basic graph; determining a screen space coordinate of a preset virtual light source and a first distance between the preset virtual light source and the three-dimensional model object, and constructing a first light shade based on the screen space coordinate and the first distance; obtaining a first outer stroke effect picture of the three-dimensional model object, and obtaining a second outer stroke effect picture of an outer stroke located in the first light shade illuminated by a preset virtual light source based on the first outer stroke effect picture and the first light shade; the post-processing effect picture is obtained based on the scene basic picture and the second outer stroking effect picture, and the post-processing effect picture is displayed, so that the picture coordination degree and the style flexibility are improved, the problem of visual separation of light and stroking is solved, and the quality, controllability and efficiency of the post-processing effect are improved.

The invention relates to the technical field of fiber performance test and evaluation, in particular to a method for rapidly measuring the fiber breakage rate of a fiber bundle by combining a computer and amplification equipment. The invention provides a rapid detection method of a fiber end breakage rate. The detection method comprises the following steps: S1, preparing a sample; s2, obtaining a fiber cross section; s3, automatically identifying the image; and S4, calculating the fiber end breakage rate. According to the method, a laser confocal microscope is adopted to amplify the fiber cross section and continuously photograph the fiber cross section for multiple times, computer image analysis software is adopted to automatically identify and integrate the obtained whole bundle of fiber cross sections, overlapping areas among photos are removed, and the complete and accurate fiber cross section is obtained. Metallographic software is adopted to identify and mark the cellosilk, so that the rapid and accurate measurement of the cellosilk is realized.

The invention relates to the technical field of computer image processing, and particularly discloses a multi-image intelligent compression splicing method and splicing system capable of configuring the column number, and the splicing method comprises the following steps: 1, achieving the intelligent compression of images through the iteration quality attenuation, and balancing the image quality and storage space; step 2, dynamically calculating an image layout size based on the number of columns configured by a user; 3, keeping the aspect ratio of the image by adopting a cell centering strategy; and 4, realizing multi-image splicing by a dynamic canvas synthesis technology. The method has the advantages that intelligent image compression is achieved, and mass and storage space are balanced; a compression strategy can be dynamically adjusted according to the number of images; maintaining the original aspect ratio of the image; flexible multi-column image arrangement is supported; and splicing parameters can be dynamically configured.

The invention belongs to the computer image processing technology, and particularly relates to an intelligent image recognition and analysis system which comprises an image preprocessing module, a multi-scale feature extraction module, a feature fusion and screening module, an image classification and recognition module, an image analysis and understanding module, a real-time feedback and optimization module and the like. Multi-scale feature extraction and feature screening are respectively realized through an improved convolutional neural network and an attention mechanism, and a corresponding algorithm formula is given. And a deep learning classification algorithm, a graph neural network and a semantic analysis algorithm are adopted to improve the image classification and understanding ability. And in combination with technologies of data enhancement, transfer learning, integrated learning and the like, the system is optimized in real time by utilizing reinforcement learning, so that the image recognition accuracy, robustness and analysis depth can be effectively improved.

The invention relates to the technical field of computer image processing, and particularly provides a space-frequency domain pedestrian gait feature extraction method based on spiking neural network driving, and the method comprises the steps: carrying out the gait feature extraction of a gait image through employing a space-frequency domain pedestrian gait feature extraction model; the space-frequency domain pedestrian gait feature extraction model comprises a backbone network, a multi-stage time domain global feature interaction enhancement module and a distinguishing feature aggregation module; the backbone network comprises N stages which are connected in sequence, and each stage comprises a space-frequency feature fusion module for fusing space-domain features and frequency-domain features; a multi-stage time domain global feature interaction enhancement module extracts and enhances spatial domain global features and time domain global features; and the distinguishing feature aggregation module aggregates the output of the backbone network and the output of the multi-stage time domain global feature interaction enhancement module. According to the method, the capturing capability of the model on gait micro-detail features can be improved, and the adaptability to complex scenes is enhanced.

The invention relates to a vaulting horse mark-free image processing and intelligent motion capturing system based on a computer vision technology, and belongs to the field of computer image processing, and the system comprises a plurality of cameras which are used for collecting vaulting horse motion video images of a moving target at a fixed point; the image processing and intelligent motion capture device respectively extracts feature points from different camera video images, adopts machine vision and deep learning technology to automatically identify human body articulation points in motion, tracks motions in motion, analyzes the motions in real time, reconstructs and obtains three-dimensional space coordinate information of a moving target, and carries out real-time tracking on the three-dimensional space coordinate information of the moving target. And performing optimization processing on the three-dimensional space coordinate information to obtain motion capture data. The system provided by the invention can realize integrated operation of video acquisition, motion capture and key data extraction under a large-view-field complex background.

The invention relates to the field of computer image compression, in particular to an image compression method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a current compression demand, a current compression rule, a current variable rendering rate parameter and a to-be-compressed image, and determining a target compression rate curve according to the current compression demand and the current compression rule, and generating a first pixel coordinate mapping function according to the target compression rate curve and the current variable rendering rate parameter, and performing mapping compression on the to-be-compressed image based on the first pixel coordinate mapping function to obtain a compressed image. Therefore, the image is compressed by using the pixel coordinate mapping function, the problems that a large number of computing resources need to be consumed, the storage space requirement is large and the display effect of the compressed image is poor when the image is compressed at the present stage are solved, and the visual experience of a user is improved.

The invention relates to the technical field of computer image restoration, in particular to a frequency domain and spatial domain adaptive collaborative modeling image defogging method, which comprises the following steps: acquiring a foggy image, and inputting the foggy image into a trained image defogging model to obtain a defogged image; the training process of the image defogging model comprises the following steps: inputting a foggy image into the shallow feature extraction module to obtain a preliminary feature; inputting the preliminary features into a frequency domain enhanced attention encoder to obtain advanced features; inputting the advanced features into a frequency domain enhanced attention decoder to obtain residual features; adding the foggy image and the residual features of the foggy image to obtain a defogged image; calculating a loss function value according to the fogless image and the defogged image of the foggy image, updating parameters of the model according to the loss function value, and obtaining a trained model when the loss function value is minimum; according to the method, a frequency domain attention enhancement module is introduced in the encoding and decoding processes, a defogging model for frequency domain and space domain adaptive collaborative modeling is constructed, and the defogging stability and definition are improved.