93 results about "Texture enhancement" patented technology

The invention discloses an image super-resolution method and system based on semantic perception tokens, and relates to the technical field of computer vision, and the method comprises the steps: generating semantic confidence and grouping information through the aggregation of content perception tokens, and decoupling a basic residual error into a texture enhancement and degradation inhibition guidance graph; in combination with a static semantic constraint mask and a sparse matrix multiplication mechanism, progressive focusing of attention is realized; a diffusion time step embedding and cooperative modulator is introduced, semantic guidance information is dynamically injected into a multi-step denoising process, adaptive attention features and diffusion reconstruction features are fused, and finally a high-fidelity and high-resolution image is output. According to the method, content-adaptive high-resolution image reconstruction is realized through collaborative modulation of a sparse attention mechanism guided by semantic grouping and diffusion denoising guided by semantic decoupling.

The invention discloses a parallel U-Net-based dual-domain collaborative infrared and visible light image fusion method. The method comprises the following steps: firstly, extracting initial features of a source image by using dense connection blocks; then, parallel frequency domain branches and spatial domain branches are constructed, the frequency domain branches are combined with discrete wavelet transform and fast Fourier convolution to decompose and enhance multi-scale global frequency domain features, and the spatial domain branches capture long-distance spatial dependence with linear calculation complexity by using a convolutional layer and Mama based on a selective state space model; dynamic interaction and weighted fusion of double-domain information are realized through an adaptive feature fusion module; and finally, generating a fused image through an image reconstruction module. According to the method, the problems of high calculation overhead and video domain information negligence in the prior art are solved, and infrared heat radiation maintenance and visible light texture enhancement are effectively considered.

The invention discloses a corn disease recognition method based on an improved generative adversarial network. The corn disease recognition method comprises the steps that S1, an initial low-resolution image of a corn field is collected and obtained through an unmanned aerial vehicle; s2, performing super-resolution reconstruction on the initial low-resolution image by using an improved generative adversarial network model; the model construction comprises the following steps: S2.1, constructing a shallow feature extraction layer; s2.2, constructing a deep feature extraction network based on a plurality of RRDB nested residual dense blocks; s2.3, constructing an attention module based on a space and channel dual attention mechanism; s2.4, a multi-scale texture enhancement module is constructed through multi-scale convolution and smooth branches; s2.5, constructing a global residual connection layer; s2.6, constructing an adaptive hybrid up-sampling module based on transposed convolution and stable up-sampling; s2.7, performing mapping output on the features after up-sampling; and S3, carrying out disease prediction on the high-resolution reconstructed image. According to the method, details such as spatial resolution and texture of the unmanned aerial vehicle high-altitude flight remote sensing image are improved, and then the corn disease monitoring precision is improved.

The invention relates to the technical field of computer graphics and deep learning, in particular to a simulation bait automatic coloring method and system based on a 3D model. The method comprises the following steps: acquiring an uncolored 3D model and a reference image, and generating standard data through analysis verification, curvature grid division and image compliance detection; performing color conversion, texture enhancement and multi-scale downsampling on the compliant image to construct an image pyramid; performing multi-level feature extraction and adversarial training optimization based on a pre-trained convolutional neural network and a generative adversarial network, and generating an enhanced color texture map; performing UV expansion, color mapping and normal mapping fusion in combination with the model topology, and constructing an intermediate model with physical rendering attributes; and batch color consistency verification is realized through color histogram comparison, adaptive threshold segmentation and iteration parameter adjustment, and a standard model group is generated. According to the invention, efficient and highly realistic automatic coloring of the simulated bait is realized, and color consistency and rendering quality in batch production are guaranteed.

The invention provides a remote sensing image rotating target detection method based on dual-path feature enhancement, and relates to the technical field of computer vision and remote sensing image processing. The method comprises the following steps: constructing a dual-path feature enhanced remote sensing image rotating target detection network comprising a texture enhancement path and a direction modeling path; wherein in the texture enhancement path, a self-adaptive wavelet reconstruction module is adopted to enhance texture details and edge features in the input feature map; in the direction modeling path, performing spatial alignment and direction consistency modeling on the input feature map by adopting a multi-scale angle guide deformable encoder, and extracting features containing structure and direction information; fusing the features output by the two paths; constructing a joint loss function, and performing end-to-end training on the network; and detecting and positioning a rotating target in the remote sensing image by using the trained network. By adopting the method, the detection precision and robustness of multi-direction, multi-scale, densely distributed and small-size targets in the remote sensing image can be effectively improved.

The invention relates to the technical field of image enhancement, in particular to a dark field detail dynamic enhancement method and system for an LED backlight source, and the method comprises the following steps: setting a brightness threshold T1 and a brightness threshold T2 according to the brightness component of an input image frame, the dynamic backlight range of the associated LED backlight source and a gamma curve; according to the method, the pixel brightness is subjected to fine-grained division through partition judgment of the brightness component of the input image frame, so that more accurate dark field area positioning is realized, and the local linear transformation coefficient is constructed for the dark field mask area by using the original brightness component, so that the subsequent enhancement operation has continuity and edge retention characteristics; and adaptive classification of the image content is realized through a structure complexity threshold value, then differential enhancement strategies such as multi-direction texture enhancement, normal direction sharpening and curved surface smoothing are extracted respectively, and gain distribution conforming to different region characteristics is established, so that the dark field detail identification granularity is higher.

The invention discloses a ventilation equipment blade quality detection method based on image processing, relates to the technical field of image processing quality detection, and is used for solving the problem of inaccurate blade defect detection. According to the method, the original image of the blade is obtained by adaptively adjusting exposure time, industrial camera gain and a controllable light source combination, reflection and shadow interference is inhibited by adopting extreme region masking, multi-frame compensation and local brightness balance, a contour enhancement image and a texture enhancement image are generated, an outer contour and a skeleton are extracted on the basis of a design contour, and the texture enhancement image is obtained. Re-sampling the texture along the skeleton and the normal direction, constructing a blade surface expansion view and a quality analysis coordinate system, detecting multiple types of surface defects in an expansion domain, forming a blade quality feature vector by combining chord length, elongation, section width and bending deviation, outputting a quality grade and a disposal suggestion, and realizing unified quantification of geometric deviation and surface defects of the blade. And the online detection stability and accuracy are improved.

The invention discloses a method for identifying early damage of a floor support plate. The method comprises the steps of image data acquisition and marking, texture enhancement preprocessing based on multi-scale gradient guidance, damage probability thermodynamic diagram generation fused with physical prior, double-flow feature interaction backbone network construction, and multi-scale deformable feature aggregation and damage perception channel weighted classifier design. And performing multi-task dynamic weighted loss optimization and model training. Image enhancement and mechanical strain prior information are fused in an input stage, and a self-adaptive interaction mechanism of global and local features is introduced into a network structure, so that the recognition capability of early damage such as microcracks, local corrosion and connection looseness is effectively improved. Experimental results show that the method shows higher detection precision and robustness under the conditions of complex backgrounds and multiple damage levels, and is remarkably superior to an existing convolutional neural network and a conventional detection model.

The invention discloses an automobile covering part surface abnormal defect synthesis and detection method based on illumination condition constraint. The method comprises the following steps: acquiring an automobile covering part defect data set; an illumination condition continuous mapping table is constructed, bilinear interpolation sampling is carried out on standardized sampling coordinates containing illumination conditions and defect types to obtain high-dimensional vectors, and encoding and mapping of defect sensing illumination conditions are achieved; a UNet adaptive modulation network integrated with an illumination perception residual block is constructed, abnormal texture enhancement is realized through the adaptive modulation network, and finally, the mask, the normal image and the abnormal texture enhanced image are fused to perform synthesis of an abnormal sample; a pre-trained backbone Wide ResNet50 is used as a feature extractor to carry out multi-scale feature aggregation on an illumination perception anomaly synthesis sample, a dichotomy discriminator is trained to discriminate the aggregated multi-scale features, and a trained model is used to carry out anomaly detection and positioning on a test chart. According to the method, the illumination condition is used as an optimizable control variable to be deeply fused into an abnormal synthesis and detection framework, illumination controllable abnormal synthesis, illumination robust feature learning and high-precision defect positioning are realized, and an effective solution is provided for industrial appearance detection under complex illumination.

The embodiment of the invention provides a model training and image processing method and device, a storage medium and a program product. In the embodiment of the invention, the target degradation parameter is obtained by optimizing the initial degradation parameter with the target that the texture loss after image degradation processing is smaller than or equal to the set texture loss threshold value, so that the target degradation parameter is obtained according to the target degradation parameter obtained through optimization. The texture loss of the input image of the model obtained by carrying out degradation processing on the target image is smaller than the texture loss threshold compared with the target image, so that a moderately degraded training sample is generated, and the degraded image still keeps some texture information to provide reliable context clues for the model; and texture reasoning can be carried out based on real observation instead of generating some artifacts, so that texture enhancement is effectively realized.

The invention relates to the technical field of image processing and crack recognition, in particular to a cooling tower crack intelligent recognition system based on an unmanned aerial vehicle inspection image, which comprises an image acquisition module, a self-adaptive distortion correction module, a local texture enhancement module, a crack feature focusing module and a geometric morphology recognition and positioning module. Wherein the image acquisition module is used for acquiring an original image of the cooling tower; the self-adaptive distortion correction module is used for carrying out nonlinear geometric correction; the local texture enhancing module is used for enhancing the texture in the crack direction and inhibiting concrete background noise; the crack feature focusing module is used for extracting a pixel-level crack candidate region; and the geometrical morphology identification and positioning module outputs a crack data set with spatial position information. According to the method, through combination of image geometric correction, texture enhancement and three-dimensional space mapping, accurate extraction, spatial positioning and actual width measurement and calculation of the cooling tower cracks are realized, and the accuracy and reliability of structural defect identification are improved.

The invention discloses a plastic product flaw online detecting and sorting system based on machine vision, and particularly relates to the technical field of plastic flaw detection.The plastic product flaw online detecting and sorting system is characterized in that an image acquisition module acquires multi-angle original images under polarized light illumination and establishes a reflection response indexing relation; the spectral reflection structure shaping module implements differential illumination processing based on the indexing relation to generate a primary processing image; the feature enhancement processing module constructs three types of defect structure response channels, and generates a defect feature enhanced image through multi-scale texture enhancement; the defect identification module introduces a material adaptation factor and combines plastic physical characteristic correction to realize accurate defect discrimination; and the intelligent sorting execution module completes online grading sorting according to the defect identification indexes. The system is suitable for plastic products with different transparency, refraction coefficients and surface roughness, the problems that traditional detection is poor in adaptability, low in precision and inaccurate in sorting are effectively solved, the industrial real-time production requirement is met, and the product quality and the production efficiency are improved.

The invention provides a depth estimation method based on a double-branch deep network and multi-attention fusion, relates to the technical field of image processing, is applied to a depth estimation network trained through self-supervised learning in advance, and comprises a detail branch module, a global branch module, a texture enhancement module, a sliding window self-attention module and a depth prediction head. The method comprises the following steps: acquiring an image needing depth estimation; respectively inputting the image into a detail branch module and a global branch module to obtain an initial detail feature and an initial global feature; inputting the initial detail features and the initial global features into a texture enhancement module to obtain enhanced detail features of the image; inputting the initial global feature into a sliding window self-attention module to obtain an enhanced global feature of the image; the enhanced detail features and the enhanced global features are spliced and then input to a depth prediction head to obtain a depth map, and the accuracy of depth estimation is effectively improved by fully extracting the detail features and the global features in the image.

The invention discloses a low-light image enhancement method based on multi-branch feature fusion. The method comprises the steps of obtaining low-light and normal-light images, and dividing training and test sets; processing the normal light image to obtain a single-channel color-enhanced, brightness-enhanced and texture-enhanced component graph; obtaining a multi-branch feature fusion low-light image enhancement neural network model; and inputting a to-be-measured low-light image into the model to enhance the backbone network, and outputting a restored image under a normal illumination condition. According to the invention, a color dynamic calibration module is provided, and a plug-and-play color correction normal form is provided for the model; an external source wound modulation module is provided, and key information can be focused while interference information such as noise is restrained. A brand new shared convolution is provided, so that the model can adaptively adjust the characteristic response intensity of each channel. An exogenous creative shared residual block structure is constructed, so that the enhanced backbone network can utilize the internal feature abstraction ability and the external auxiliary information guide advantage at the same time.

The invention discloses an optical cement surface defect detection method and system based on visual inspection. The method comprises the steps that an original image set is acquired and preprocessed to obtain an intermediate image sequence; performing dynamic noise estimation on the intermediate image sequence, and selecting a high-frequency noise region in the intermediate image sequence for optimization to obtain a stable image sequence; defect detection is carried out on the stable image sequence, edge texture enhancement processing is carried out after preliminary defect area distribution is obtained, coordinates are mapped, and defect positioning coordinates are obtained; performing real defect judgment on the image according to the defect positioning coordinates to obtain a defect aggregation degree, dividing a high-density aggregation region, extracting the defect size of the high-density aggregation region, and generating a defect distribution mapping graph in combination with the defect aggregation degree; and carrying out image superposition according to the mapping graph and the original image set, and carrying out risk assessment on the high-density defect area according to the superposed image to obtain a final defect detection report. According to the method, accurate detection and analysis can be ensured when tiny defects are processed.

The invention provides a metal additive mold defect detection method and system based on a wavelet function, and relates to the field of graph attribute analysis. The method comprises the following steps: firstly, acquiring a multi-dimensional surface image of the metal additive mold from an MES (Manufacturing Execution System) through a network interface; secondly, a homomorphic filtering algorithm is adopted to carry out normalization processing on the metal additive mold image with non-uniform illumination, and non-uniform illumination correction and texture enhancement are achieved; thirdly, performing three-layer decomposition on the processed image by using a DB4 wavelet function, and calculating an energy ratio of each frequency band; then, by constructing a self-adaptive threshold model and morphological operation, metal additive mold defect analysis is carried out based on the abnormal metal surface image features, automatic detection of the metal additive mold is achieved, and a detection report is generated; and finally, calling an REST interface to automatically report a metal additive manufacturing defect detection result to an MES system.

The invention relates to an AI-based binocular double-screen display enhancement method and device, and the method comprises the following steps: carrying out the collection and distortion correction of a target scene through a binocular camera, and obtaining a binocular correction image; graying the image into a binocular grey-scale image, and carrying out pixel block matching based on the binocular grey-scale image to obtain a pixel matching pair; and calculating a left and right target pixel coordinate difference value according to the matching pair, generating a scene disparity map, and extracting scene depth data from the scene disparity map. And mapping the binocular correction image to a left display screen and a right display screen of the double-screen display equipment by using the depth information to form a double-screen initial image. And performing brightness consistency adjustment on the double-screen initial image to obtain a brightness balanced image. The edge detail enhancement including contrast adjustment and texture sharpening is performed on the brightness-balanced image based on the scene depth data, and then the processed edge enhancement region map and the texture enhancement image are fused, so that the technical problem of visual discomfort caused by lens distortion and non-uniform illumination in an actual scene in the traditional technology is solved.

The invention discloses a tiny focus recognition method for prostate puncture and application, and the method comprises the steps: S1, collecting and preprocessing an MRI image and an ultrasonic image of a patient, and obtaining a preprocessed MRI image and a preprocessed ultrasonic image; s2, constructing a texture attention residual network, and extracting MRI texture enhancement features; constructing a multi-scale edge response gating network, and extracting ultrasonic edge enhancement features; s3, performing spatial transformation and resampling on the ultrasonic edge enhancement features to obtain aligned ultrasonic enhancement features; s4, calculating a focus area weight feature matrix, an MRI dynamic weight matrix, an ultrasonic dynamic weight matrix, redundancy-removed MRI features, redundancy-removed ultrasonic features and tiny protection features; and S5, identifying the tiny focus area, and controlling the puncture robot to puncture. The problems that a traditional method is poor in tiny focus recognition capacity, missed diagnosis of the focus is prone to occurring, and puncture accuracy is insufficient can be solved.

The invention belongs to the technical field of surface defect detection, and particularly relates to a defect detection method and system based on texture enhancement and dynamic pseudo label screening, and the method comprises the steps: obtaining an industrial surface defect image, constructing a data set, and dividing labeled and unlabeled data; a defect detection network is constructed, a texture enhancement module is introduced into backbone shallow layer features, and the reliability of pseudo labels is improved; constructing a teacher and student detector under a teacher-student self-training framework, and updating teacher parameters by index moving average; generating a weak / strong enhanced view for the unlabeled image, and generating candidate pseudo labels for the weak enhanced view by a teacher; determining a category adaptive threshold based on confidence distribution, and dynamically screening pseudo labels according to categories; training students by combining screened pseudo labels with labeled data, and iteratively updating parameters; and outputting a defect category and a positioning result after training is completed. Compared with the prior art, the method has the advantages that pseudo label noise can be inhibited, class imbalance can be relieved, the detection precision is improved under a low label proportion, and the method is suitable for industrial defect detection scenes.

This invention relates to the fields of artificial intelligence and image recognition technology, specifically to an image recognition method and system for identifying missing key components in discarded switchgear. The method comprises the following steps: collecting image data of discarded switchgear; labeling the images; dividing the labeled image dataset into training, validation, and test sets; performing illumination normalization and geometric adaptive alignment on the original images of the discarded switchgear to obtain adaptively normalized images; enhancing the texture of the adaptively normalized images and fusing them with occlusions to output an occlusion-synthesized enhanced image; constructing and training a component missing identification model; calculating the model's loss function and iteratively training the model to obtain a trained model; and inputting newly collected data, after processing in steps S2 and S3, into the trained model to obtain the final missing component identification results. This invention can achieve accurate identification of missing key components in discarded switchgear.

This invention relates to the field of medical image processing and intelligent monitoring technology, specifically to a postoperative rehabilitation effect monitoring system for general surgery based on image enhancement. The system includes: a spatiotemporal feature decoupling step: constructing a dynamic deformation field model, decomposing time-series image data containing non-rigid deformation into a mechanical deformation feature layer and a physiological texture feature layer, and calculating the texture deformation decoupling coefficient; an illumination consistency normalization step: calculating the time-series illumination consistency variance, and performing illumination component correction on the time-series image data based on the variance; an adaptive texture enhancement step: performing weighted enhancement on the physiological texture feature layer according to the decoupling coefficient, generating an enhanced rehabilitation texture image; and an evolutionary trend analysis step: extracting the orderliness index of texture evolution based on the enhanced image, generating monitoring results. This invention effectively solves the interference of mechanical deformation on the analysis of microscopic healing textures, significantly improving the image signal-to-noise ratio and feature extraction accuracy.

The invention relates to image optimization, in particular to an image quality optimization method based on multi-branch collaboration, which comprises the following steps of: inputting a denoising feature into a local information enhancement branch, and enhancing local texture information of a dark region to obtain a local enhancement feature while keeping the stability of an original structure of a low-light image; inputting the de-noising features into a global information enhancement branch, and performing global color information modeling and compensation on the de-noising features for the difference of the low-light image in the aspects of overall brightness distribution and color consistency to obtain color features; denoising features are input into a texture enhancement branch, high-frequency structure information is intensified in a targeted mode through a feature enhancement mechanism combining explicit texture guidance and implicit attention modeling, so that lost detail edge and local texture information of a low-light image after denoising is restored, the definition and visual detail expressive force of a finally reconstructed image are improved, and the image quality is improved. Obtaining texture features; the method can overcome the defect that brightness enhancement, texture recovery and color correction cannot be realized at the same time.

This invention relates to the field of satellite remote sensing image processing and meteorological big data analysis technology, specifically a satellite sea surface temperature (SST) reconstruction method based on a spatiotemporally constrained diffusion model. First, it acquires missing Himawari L3 satellite data and OSTIA L4 global data covering a global sea area. Then, it preprocesses the missing Himawari L3 and OSTIA L4 data to obtain several slice data points. Finally, it inputs the preprocessed slice data into a trained spatiotemporally constrained diffusion model, which reconstructs the slice data using a weight file. The reconstructed slice data undergoes texture enhancement and large-scale reconstruction optimization to obtain complete reconstructed data covering the entire sea area. This invention significantly improves the texture restoration accuracy and physical consistency of satellite SST under continuous, large-scale cloud cover.

This application discloses a method for detecting apple disease spores using microscopic data based on multimodal and semi-supervised learning. The method includes: acquiring raw microscopic data of apple disease fungal spores, performing edge detection and texture enhancement to generate texture-enhanced data; extracting features from the raw microscopic data and texture-enhanced data using a dual-branch encoder, and fusing them using a cross-attention mechanism to obtain enhanced visual features; inputting the textual description information of the disease fungal spores into a text encoder for encoding to obtain global text features; aligning the enhanced visual features and global text features across modalities based on a multimodal object detection network, outputting multimodal fused features, and inputting them into a semi-supervised learning framework to train a student-teacher model using labeled and unlabeled data; inputting the microscopic data to be detected into the trained model and outputting the detection results. This method improves the detection accuracy and robustness of microscopic data with extremely low annotation costs.

The specification discloses a forgery detection method and device, a storage medium and an electronic device, wherein the method comprises: extracting a multi-class object feature map of target object data through the forgery detection model, and performing feature texture enhancement based on the multi-class object feature map to obtain a texture-enhanced feature map; performing attention feature enhancement on the object feature map through the forgery detection model to obtain an attention feature map; then performing feature attention fusion based on the object feature map, the texture-enhanced feature map and the attention feature map to obtain object fusion features; and performing forgery detection based on the object fusion features to output an object forgery detection result.

The application discloses an image super-resolution method based on omnidirectional space feature learning, a terminal and a storage medium, and relates to the technical field of image processing. The method comprises the following steps: using an encoder network to extract an initial shallow feature map of an input low-resolution image; inputting the initial shallow feature map into a deep feature extraction network composed of a plurality of cascaded omnidirectional feature extraction modules to perform spatial relationship perception feature extraction, channel relationship perception feature extraction and multi-scale detail perception feature extraction on the initial shallow feature map, and obtaining an omnidirectional feature map; inputting the omnidirectional feature map into a high-frequency texture enhancement module to perform frequency modulation feature enhancement and sparse non-local feature extraction on the omnidirectional feature map, and generating a texture enhancement feature map; performing context-aware feature aggregation on the texture enhancement feature map to obtain an aggregated feature vector; and performing implicit decoding and image reconstruction on the aggregated feature vector to obtain a high-resolution image. The application can comprehensively capture image features, effectively restore high-frequency textures and intelligently aggregate features.

The application discloses a living body attendance anti-fake method and system based on vein and facial feature hierarchical fusion, relates to the field of living body anti-fake identification, collects multiple palm images, optimizes the images through a vein texture enhancement algorithm, acquires a bifurcation point set through an improved U-Net network, completes vein living body inspection and feature matching to obtain a vein user hash; generates random instructions and collects multiple facial images, completes facial living body inspection and feature matching, and the facial user hash needs to be consistent with the vein user hash; dynamically determines a confidence weight ratio, screens optimal palm and facial images for consistency inspection, and integrates the user hash, a time period and the confidence weight ratio into an attendance record to upload a database, thereby guaranteeing the authenticity and security of the attendance result.

The application provides a remote sensing image rotating target detection method based on double-path feature enhancement, and relates to the technical fields of computer vision and remote sensing image processing. The method comprises the following steps: constructing a double-path feature enhancement remote sensing image rotating target detection network comprising a texture enhancement path and a direction modeling path; in the texture enhancement path, an adaptive wavelet reconstruction module is used to enhance the texture details and edge features in the input feature map; in the direction modeling path, a multi-scale angle guided deformable encoder is used to perform spatial alignment and direction consistency modeling on the input feature map, and extract features containing structure and direction information; the features output by the two paths are fused; a joint loss function is constructed, and the network is trained end-to-end; and the trained network is used to detect and locate the rotating targets in the remote sensing image. The application can effectively improve the detection accuracy and robustness of multi-direction, multi-scale, densely distributed and small-size targets in the remote sensing image.

The application belongs to the field of three-dimensional reconstruction, and particularly relates to a micro-morphology analysis method based on wavelet multi-layer frequency band decomposition. The method comprises the following steps: firstly, a multi-focus image sequence of a micro scene to be measured is collected, and multi-scale spatial features are obtained through feature coding and projection mapping; then, a two-dimensional discrete wavelet transform method is introduced, the spatial features are decoupled into a low-frequency approximate subband representing global geometry and a high-frequency detail subband representing fine texture, and structure feature correction and directional texture enhancement are respectively implemented; on this basis, inverse discrete wavelet transform and residual fusion are used to construct time-frequency enhanced features, a deep regression network is used to analyze pixel-level focus probability distribution to generate a continuous depth map; finally, a cascading mechanism driven by three-dimensional geometric reconstruction for scene morphology perception is established, and depth geometric edge constraints are used to reversely guide the high-fidelity generation of a full-focus image. Through the wavelet multi-layer frequency band decomposition strategy, the application effectively solves the problem of confusion between texture details and out-of-focus noise in micro imaging, and realizes the synchronous robust analysis of high-precision geometric structure and full-depth clear texture of micro morphology.

The invention relates to the technical field of image restoration, in particular to an image restoration and enhancement processing system and method, and the system comprises a boundary weight construction module, a direction gradient judgment module, a structure communication reconstruction module, a path priority control module and a detail amplitude regulation and control module. According to the method, the response coefficient is constructed by using the gray inversion frequency, the dominant direction is determined by combining the gradient direction energy difference, the reconstruction path sequence is determined by using the edge connectivity, the reconstruction priority is adjusted by using the path gradient stability, and the mapping relation between the regional texture density and the detail enhancement proportion is fused. Accurate recognition of the structure extension direction of an image defect area and dynamic scheduling of a complementation path are realized, the continuity and connection stability of an edge structure are improved, the distribution consistency and area adaptability of texture details are enhanced, the coordination of texture enhancement and edge reconstruction under a complex background is optimized, and the reconstruction efficiency is improved. And the overall restoration quality and the visual restoration degree of the image are improved.