760 results about "Kernel (image processing)" patented technology

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

The invention relates to the technical field of image processing, in particular to a dual-branch coding desert segmentation model network structure based on structure state space duality, which adopts multi-dimensional dynamic convolution to replace traditional convolution in the initial stage of an encoder, introduces a mamba2 module based on the structure state space duality into the backbone design of the encoder, and improves the robustness of the encoder. The efficiency and adaptability of the model are remarkably improved, a double-branch parallel design is adopted, one branch uses cavity convolution to extract multi-scale context information, the other branch reinforces feature expression through a mamba2 module, the model is connected in series with a space attention module and a channel attention module between an encoder and a decoder, and the algorithm is more accurate. The method has the advantages that the method is simple and easy to implement, interference of irrelevant information on segmentation results is suppressed, a deformable large kernel attention module is introduced to the tail end of a decoder, and global and local modeling capability of the model in processing desert complex boundary regions is effectively improved by combining flexibility of deformable convolution and global receptive field characteristics of large kernel convolution.

The invention discloses a mandibular neural tube CBCT panoramic image segmentation network training method based on a Mama architecture, and relates to an image processing technology in the field of computer vision. According to the segmentation network training method, an encoder and decoder architecture is adopted, tubular structure features are extracted by enlarging a receptive field and learning deformation based on feature extraction operation of a tubular convolution kernel, and then a three-vision feature fusion strategy is carried out to further guide a model to supplement attention to key features from different angles; the encoder part models global and multi-scale features through a Mama architecture, and keeps efficient in training and reasoning processes, and finally enables the network to have a more complete segmentation effect on a CBCT image of a tubular structure.

The invention relates to the technical field of image processing, in particular to a high-voltage electrical equipment surface defect identification method based on image processing. The method comprises the following steps: analyzing the gradient of pixel points in a to-be-analyzed image of a to-be-detected area on the surface of the high-voltage electrical equipment to obtain the weight of each pixel point; weighting the gray value of each pixel point in the to-be-analyzed image subjected to Laplacian filtering by using the weight of each pixel point of the to-be-analyzed image to obtain a first feature map; calculating a local standard deviation of each pixel point in the to-be-analyzed image so as to obtain a first parameter and a second parameter; constructing two Gaussian kernels based on the first parameter and the second parameter to filter the to-be-analyzed image to obtain a second feature map; fusing the first feature map and the second feature map of the to-be-analyzed image to obtain a defect saliency map of the to-be-analyzed image; and recognizing a surface defect area of the high-voltage electrical equipment based on the defect saliency map of each to-be-analyzed image. According to the invention, the accuracy of high-voltage electrical equipment surface defect identification can be improved.

The invention belongs to the technical field of image processing, and particularly relates to a titanium cylinder inner wall defect detection method and system.The method comprises the steps that a titanium cylinder inner wall image is collected, and a highlight area and boundary pixel points thereof are extracted through threshold segmentation and corrosion operation; calculating a scale adjustment factor by combining a highlight area proportion and a boundary gradient amplitude, dynamically scaling the reference kernel according to the scale adjustment factor, and determining a standard deviation of a multi-scale Gaussian kernel; constructing a Gaussian kernel function of each scale, and calculating the sum of difference values of the titanium cylinder inner wall image and convolution results of all scales in a logarithm domain to obtain an enhanced titanium cylinder inner wall image; and performing threshold segmentation on the enhanced titanium cylinder inner wall image to identify a defect area. According to the method, the filtering scale is adjusted in a self-adaptive mode, mirror reflection interference is effectively restrained, halo artifacts are eliminated, and tiny defects covered by highlight are accurately recognized.

The invention relates to the technical field of medical image processing, and discloses a human body abdominal fat analysis method based on a medical image, which comprises the following steps: acquiring multi-modal medical image data such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and ultrasonic images, constructing a fusion database through multi-scale affine transformation alignment, de-noising by using a model based on a residual self-encoder, and enhancing a boundary in combination with Canny edge detection. A segmentation model is constructed based on an improved U-Net architecture, multi-modal features are fused, and a dynamic convolution kernel and a channel attention mechanism are used for optimization. A quantitative analysis model is established by adopting a multi-task joint learning framework, gradient conflicts are solved, and a lightweight sub-network is searched and generated. Carrying out uncertainty modeling on the segmentation model, and carrying out active learning annotation to optimize the performance. A multi-granularity feature fusion framework is designed, anatomical priori knowledge is embedded to construct an association graph, a structured analysis report is generated, abdominal fat can be accurately analyzed, and diagnosis and treatment of obesity-related diseases are assisted.

The invention discloses a morphological gradient region replacement method based on SAM semantic segmentation and user guidance, and relates to the technical field of computer vision and image processing, and the method comprises the steps: carrying out the semantic segmentation of a to-be-processed image through an SAM model, extracting a multi-level semantic feature, carrying out the standardization and dimension reduction, extracting a causal factor based on independent component analysis, and carrying out the user guidance. A directed causal factor association graph is generated through Granger causal relationship test, and a causal attribution probability graph is generated through reverse mapping; constructing a structured causal graph, and generating a causal mask through a graph convolutional network; encoding the original interaction signal into a guide thermodynamic diagram; constructing a diffusion equation, forming a gradual change control equation by dynamically fusing and guiding the intensity distribution of the thermodynamic diagram and an image semantic diffusion item, and iteratively solving the gradual change control equation; generating an anisotropic morphological operation kernel according to the geometric curvature characteristics of each region in the replacement mask; and fusing the optimized replacement mask with the target content based on a gradient domain optimization algorithm to generate a gradient replacement image.

The invention discloses a lightweight super-resolution system and method of an adaptive wavelet attention network, and belongs to the technical field of image processing. The system is composed of a multistage wavelet attention module, a dynamic convolution kernel generation unit, a convolutional neural network and an output unit. The method comprises the following steps of: extracting features of a low-resolution image and performing Haar wavelet decomposition; calculating a cross-scale attention weight on each high-frequency sub-band and carrying out weighted fusion to highlight details; adaptively generating a dynamic convolution kernel of a Haar wavelet basis kernel weighted combination based on the fused features, and performing directional convolution enhancement on the features; high-resolution image reconstruction is realized through a lightweight residual network and pixel rearrangement; and during training, pixel domain mean square error and wavelet coefficient compensation loss joint optimization is adopted. The parameter quantity of the system model is smaller than 450KB, a 1080p video super-resolution task can be processed on mobile equipment in real time, and the texture recovery performance is improved by about 1.2 dB compared with that of an existing lightweight model.

The invention discloses a human body infrared image small target detection method based on an improved FGLCM feature, and relates to the technical field of image processing and target detection, and the method comprises the following steps: building a boundary singularity auditing baseline under a unified time baseline, carrying out the multi-scale energy mapping of a curved surface fitting residual error of a human body infrared image, and generating a traction residual error distribution diagram; and constructing a reflection pseudo peak discriminator based on the traction residual distribution diagram, and extracting a pseudo peak kernel position by combining polarization sensitivity estimation and view angle transformation consistency constraint. According to the method, a closed-loop self-adaptive regulation and control mechanism is constructed through residual distribution auditing, pseudo peak identification, gradient registration, differential entropy enhancement and time sequence threshold adjustment, fitting abnormity and false highlight spots in the infrared image are inhibited, the accuracy and stability of small target detection are improved, and the method is suitable for a complex photo-thermal environment.

The invention discloses a fine optical flow estimation system integrating multi-scale kernel selection and a super-resolution technology. Belongs to the technical field of image processing and particularly relates to the technical field of fine optical flow estimation. The problem that the RAFT network estimation precision is reduced is solved. The system comprises a feature extraction network module, a super-resolution iteration updating module and a context network module. The feature extraction network module is used for extracting features of two adjacent frames of images and inputting the extracted related features into the super-resolution iteration updating module; the context network module is used for extracting context features of a previous frame of image in two adjacent frames of images and inputting the extracted context features into the super-resolution iteration updating module; and the super-resolution iteration updating module fuses the related features and the context features to obtain an optical flow vector diagram with super-resolution and outputs the optical flow vector diagram.

The invention discloses a construction quality defect detection method and system based on image processing, and belongs to the technical field of material surface defect nondestructive testing, and the method comprises the steps: obtaining original image data of a construction area and preset building information model data, generating a material feature map and a reflection feature map, and carrying out the denoising and illumination compensation of the original image data, and acquiring enhanced image data, delimiting a key detection area to generate a dynamic mask, executing defect identification by adopting a preset high-precision convolution kernel to generate an initial defect distribution diagram, and outputting a final defect detection result by combining defect correlation correction parameters generated by matching the initial defect distribution diagram with the defect evolution database. According to the method, a double-flow convolutional network is adopted to decouple material attributes and illumination interference, a dynamic mask is combined to focus a high-risk area, and a defect evolution database is integrated to realize mechanical simulation analysis, so that the defects in the germination period can be accurately captured in a complex construction environment, the extension trend can be predicted, and the engineering safety management and control level can be remarkably improved.

The invention discloses an image deblurring method based on a rotation perception multi-dimensional attention and blurring sensitivity self-adaptive distribution mechanism, aiming at the defects of the prior art in the aspects of complex blurring processing and image reality sense improvement, and belongs to the technical field of image processing, and the method comprises the following steps: preprocessing a collected picture to obtain a training set; training an image deblurring model by using the training set; and performing image deblurring by using the trained image deblurring model. According to the invention, by designing a plurality of innovative modules and combining fuzzy degree adaptive selection, kernel estimation and dynamic weight distribution driven by a physical model, a multi-dimensional rotation perception attention mechanism, spectrum reconstruction anti-noise deblurring and detail enhancement of perception loss optimization, the image deblurring effect and processing efficiency are effectively improved; the method is especially suitable for processing blurred images with high complexity and high resolution.

The invention discloses a super-resolution image reconstruction method based on a multi-scale large-kernel convolution double-residual neural network, which is suitable for the field of image processing, and comprises the following steps: cutting a data set, inputting a cut original low-resolution image into a preprocessing module, carrying out image normalization and data enhancement operation, and carrying out image reconstruction; generating a preprocessed low-resolution image; the preprocessed low-resolution images form a distorted image block data set, and a training set, a verification set and a test set are formed; according to an existing distorted image block data set, a super-resolution image reconstruction method based on a multi-scale large-kernel convolution double-residual neural network is constructed; and inputting the data set into the constructed multi-scale large-kernel convolution double-residual neural network to extract semantic features, and amplifying a feature map by using an up-sampling module of the model to generate a super-resolution image. According to the method, a multi-scale large-kernel convolution and double-residual structure is introduced, a visual attention mechanism is used in the neural network, the extracted features better conform to human visual perception features, and super-resolution image reconstruction is more accurate.

The invention discloses an image video super-resolution enhancement method based on a degradation generative adversarial network, and relates to the field of image processing, and the method comprises the steps: carrying out the image collection and preprocessing; building and training a super-resolution enhancement model; and carrying out super-resolution enhancement on the image based on the degradation generative adversarial network model. According to the method, an image content self-adaptive dynamic degradation kernel generation mechanism is adopted, the degradation process of the image under different equipment and organization structures is truly simulated, a dynamic up-sampling and residual error correction network guided by the degradation kernel is adopted, the detail reduction capability and the structure fidelity of the super-resolution image are remarkably improved, and the super-resolution image quality is improved. The image texture authenticity and key organization density consistency are effectively enhanced, the balance of training games between a generator and a discriminator is realized, and the model stability and convergence quality are improved.

The invention discloses a seal area character recognition method and device and a nonvolatile storage medium. The method comprises the following steps: determining a candidate seal image area in an image according to color information of pixel points in the image; point-by-point sliding convolution processing is carried out on the candidate seal image area through a multi-scale annular convolution kernel group, so that a target seal image area is determined in the candidate seal image area, and the multi-scale annular convolution kernel group comprises a plurality of convolution kernels which are of concentric ring structures and have different radius lengths; mapping the target seal image area into a rectangular expanded image, and identifying and extracting a character image to be identified in the rectangular expanded image; and performing identification processing on the character image to be identified to obtain a seal text corresponding to the target seal image area. The technical problem that the text image processing efficiency is low due to the fact that the text information of the seal area cannot be effectively recognized in the related technology is solved.

The invention discloses a change detection method based on semantic prior and fusion space positioning, and relates to the technical field of image processing, and the method comprises the steps: firstly, carrying out the pre-training of a BPNet network on a supervised remote sensing extraction task, and fully utilizing the prior knowledge in the remote sensing extraction task; and then fine tuning is performed on the pre-trained BPNet network, a twinning network structure sharing weight is adopted to expand an encoder and decoder structure, a space consistency attention module is introduced, and feature information extraction and fusion are performed on the dual-tense image by combining a self-attention mechanism and a large convolution kernel decomposition technology so as to obtain a dual-tense image. Therefore, remote sensing image target change information is obtained. Therefore, by adopting the change detection method based on semantic prior and fusion space positioning, through the space consistency attention module, the depth encoder can be helped to expand an effective receptive field, the modeling capability of a long-distance dependency relationship is enhanced, and meanwhile, double-tense change detail information can be effectively captured, and a more accurate feature map can be obtained.

The invention belongs to the technical field of image processing, and provides an image processing metasurface design method based on complex amplitude modulation, which comprises a dielectric metasurface consisting of a silicon dioxide substrate and alpha-S nano-columns on the substrate, and adopts a dielectric nano-column arrangement method based on a geometric phase physical concept. Enabling the point spread function of the metasurface to be equal to a specific convolution kernel, and carrying out image processing operation according to the principle that the response of an image through a system corresponds to the convolution of the image and the point spread function of the system; according to the method, a dielectric nano-column arrangement method based on a geometric phase physical concept is adopted, so that the point spread function of the metasurface is equal to a specific convolution kernel, edge detection and second-order differential image processing operation are carried out by utilizing the principle that the response of an image through a system corresponds to the convolution of the image and the point spread function of the system, and the detection precision of the metasurface is improved. According to the method, the size of the optical convolution layer is greatly reduced, and the integration level and the operation speed are improved.

The invention relates to a robust unmanned aerial vehicle detection method based on dynamic feature fusion and context attention, and belongs to the technical field of image processing. Aiming at the problems of small target feature loss, semantic gap, background noise interference and the like caused by a fixed convolution kernel scale, one-way feature fusion and a static attention mechanism in an existing unmanned aerial vehicle aerial image target detection method, the method comprises the following steps: constructing a detection model comprising a backbone network, a neck network and a detection head network; a feature rearrangement and extraction module is designed in the backbone network to enhance feature learning, an enhanced double-flow feature fusion pyramid is designed in the neck network to optimize multi-scale feature fusion, and a dynamic multi-scale context attention mechanism is designed in the detection head network to suppress irrelevant background noise. The method effectively improves the accuracy and robustness of small target detection, and achieves a clearer and more stable detection effect in a complex environment.

The invention relates to the technical field of medical image processing and computer vision, in particular to a parasitic ovum microscopic image detection method and system based on polymorphic prior, and the method comprises the steps: obtaining a to-be-detected microscopic image, and carrying out the feature extraction of the to-be-detected microscopic image through a convolutional neural network, and obtaining an initial feature map; constructing a polymorphic convolution kernel library based on preset biological morphological characteristics of the parasitic ova; performing deep convolution and feature fusion operation on the initial feature map by using a polymorphic convolution kernel library to generate a space attention map; performing feature enhancement processing on the initial feature map by using the spatial attention map to obtain an enhanced feature map; and performing bounding box regression and category prediction on the enhanced feature map to obtain a parasitic ovum detection result. According to the method, morphological priori and attention mechanisms are introduced, so that the problems of egg form similarity, background interference and the like are solved, accurate and robust automatic detection is realized, and the clinical diagnosis efficiency is remarkably improved.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention belongs to the field of CT image processing, and particularly relates to a CT image reconstruction method based on a three-dimensional diffusion model and a related device.The method comprises the steps that a medical CT scanner is adopted to obtain three-dimensional CT voxel data, and the three-dimensional CT voxel data is preprocessed; extracting a medical object surface point cloud from the preprocessed three-dimensional CT voxel data by adopting a voxel-level reconstruction algorithm, and generating three-dimensional sparse point cloud data; processing the three-dimensional sparse point cloud data by adopting a 3D Gaussian splashing technology, and converting the three-dimensional sparse point cloud data into three-dimensional Gaussian distribution data by using a pre-trained three-dimensional Gaussian kernel; gradually adding Gaussian noise to the three-dimensional Gaussian distribution data by adopting a three-dimensional diffusion model, and gradually removing the Gaussian noise to generate reconstructed three-dimensional Gaussian distribution data; and projecting the reconstructed three-dimensional Gaussian distribution data to a two-dimensional plane by adopting a rendering technology to generate a two-dimensional CT reconstructed image. According to the method, the advantages of the 3D Gaussian splashing technology and the three-dimensional diffusion model are combined, efficient, real-time and denoising medical image reconstruction can be achieved under the low-dose CT imaging condition, and meanwhile the problem of white floater artifacts possibly occurring in the reconstruction process is effectively solved.

The invention relates to the technical field of image processing, in particular to a picking ingot coating defect online detection method and system based on machine vision, and the method comprises the steps: obtaining a multi-view image, carrying out the feature extraction of an image data stream after preprocessing, and carrying out the matching or clustering with a preset defect dictionary; the method comprises the following steps: preliminarily identifying a potential defect area, triggering refined multi-angle image acquisition, applying a multi-task CNN model fused with a polarization perception convolution kernel, jointly optimizing pixel-level segmentation loss and boundary prediction loss, outputting pixel-level semantic segmentation and an accurate boundary of the defect area, and calculating a reconstruction error to carry out defect classification. And finally, the system also has the functions of defect root cause analysis and process flow adjustment, so that defect tracing and production optimization are realized. According to the system, advanced visual technology, deep learning and multi-modal information fusion are integrated, and online, accurate and automatic detection of picking ingot coating defects is realized.

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

The invention relates to the technical field of mars surface rocks, and particularly discloses a mars surface stone extraction method and system based on a lightweight network, and the method comprises the steps: obtaining a mars surface image shot by a mars patroller, and screening a rock image; making labels based on the rock images, performing image processing on the labels and the rock images to expand the number of the images, and dividing the rock images into a training set, a test set and a verification set; dynamically adjusting the sampling shape and size of a convolution kernel of the training set through adaptive kernel convolution so as to extract rock features in the rock image; separating the rock image from the surrounding environment by using an improved lightweight Transform model; the attention of rocks covered by sand and dust is improved through an enhanced multi-dimensional convolution attention mechanism; and evaluating the lightweight Transform model based on the pre-training index, if the evaluation passes, obtaining a final model, and based on the final model, carrying out rock surface extraction on the input Mars rock image.

The invention discloses a sparse matrix processing method and device in an image recognition process, equipment and a medium, and relates to the field of computer image processing, and the method comprises the steps: inputting a feature image into a convolutional neural network; if the convolution kernel meets a preset sparse matrix condition, performing structured sparseness on the convolution kernel to obtain a sparse matrix, compressing the sparse matrix by using an improved compression sparse row format to obtain a compression matrix, and constructing an index matrix of the compression matrix; storing the feature image, the compression matrix, the index matrix and the preset bias matrix in a global memory, activating a target thread bundle in the computing core, and distributing the target thread bundle to a target data processing channel; loading the compression matrix and the index matrix to a shared memory corresponding to the target data processing channel by using the target thread bundle, and loading the feature image and a preset bias matrix to a register file; and executing the operation instruction to read the current matrix operation data from the shared memory and the register file to the matrix calculation unit to complete the matrix multiplication and addition operation.

The invention belongs to the technical field of image processing, and particularly relates to an optical remote sensing image saliency target detection method, which comprises a feature extractor PVT, three core modules and a decoder, the feature extractor PVT uses the PVT as an encoder of ADNet, the PVT is composed of four encoder blocks of different levels, and the decoder is composed of three core modules. The three core modules comprise a random weighted space attention module, a dynamic semantic fusion module and an adaptive edge enhancement module, parameters k2 and k3 of the dynamic semantic fusion module are adaptive convolution kernels dynamically generated by SKC, and features faeem, fdsfm and fswsam generated by the above modules are input into a significance feature decoder; the random weighted space attention module improves local detail information of advanced features through a local enhancement and weighted fusion mode, effectively overcomes the defects of PVT in local information processing, and enables the model to accurately position a significant target in an optical remote sensing image.

The invention provides a medical image information alignment method and device based on graph network guidance and a medium, and belongs to the technical field of medical image processing. Converting the single-mode feature graph into a complete graph, and utilizing information transmission and aggregation characteristics of a graph neural network; updating edge weights and removing low-quality edges and nodes to obtain a plurality of sub-graphs containing important objects; constructing a cross-modal diagram; a cross attention mechanism is configured based on the patch level, patch level information matching is achieved, and differentiation between different medical images is found; defining an internal learning kernel function and an external learning kernel function; and calculating the distance between the two sample pairs in the high-dimensional space, and carrying out clustering analysis by using the distance. According to the method, attention of the model to important objects and a multi-mode matching mode are more in line with a human thinking mode, and meanwhile, a cross attention mechanism is introduced to achieve alignment of finer-grained information.

The invention discloses an unmanned aerial vehicle low-altitude photography image enhancement method and system. The method comprises the steps of data preparation, motion joint deblurring optimization, multi-source data registration, scene adaptive enhancement and image comprehensive enhancement. The invention relates to the technical field of unmanned aerial vehicle image enhancement, and realizes high-precision deblurring, precise registration and adaptive visual enhancement of an unmanned aerial vehicle image by fusing inertial measurement unit guided blurring kernel prediction, cross-modal graph structure matching and elastic transformation optimization and an enhancement strategy dynamic adjustment mechanism based on scene analysis. The image definition, the structure consistency and the scene readability are remarkably improved, and the method is suitable for image processing tasks in a complex low-altitude flight environment.

The invention discloses a wide-angle image acquisition and processing system for underwater target identification, which relates to the technical field of image processing and target detection, and comprises an image acquisition and preprocessing module for acquiring an underwater image, removing initial noise through a preset filter, processing an enhanced grayscale image by adopting image smooth combination, and obtaining an underwater image; the pixel density gradient calculation module is used for calculating a multi-direction pixel density gradient by adopting a horizontal direction convolution kernel and a vertical direction convolution kernel of a Sobel operator according to the enhanced grayscale image, and obtaining a gradient intensity graph through gradient amplitude calculation; according to the wide-angle image acquisition and processing system for underwater target recognition, through multi-level feature fusion and classification positioning, the detection precision and positioning accuracy of an abnormal region in an underwater complex environment are remarkably improved, and efficient technical support is provided for underwater target recognition.

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.