174 results about "Texture enhancement" patented technology

The invention discloses a visual detection system and method for tiny flaws of industrial products, and belongs to the technical field of product detection, multi-source image data of a target industrial product under multiple detection angles and illumination conditions are acquired, and an image information matrix is established; performing region segmentation and texture enhancement on the image, and extracting local texture direction inconsistency parameters; carrying out normalization analysis on the pixel ratio under different spectrum channels, and calculating a multispectral reflectance ratio abnormal index; constructing a deep convolution recognition model; reasoning the image by using the model, and outputting a defect judgment result and a confidence score; judging whether the area is a flaw area based on a dynamic threshold mechanism, and outputting a detection report containing flaw position information and a visual heat map; according to the method, multi-dimensional fusion identification of texture structure disturbance and spectral response abnormity is realized, the micro defect identification precision is effectively improved, and the method has high robustness, automation and engineering practicability and is suitable for high-precision quality control requirements of various industrial scenes.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system and method based on a cloud platform. The method comprises the following steps: acquiring multispectral video image data of an industrial site; analyzing the difference characteristics of visible light and infrared spectrum based on the original data of the multispectral video image, identifying the characteristic mode of electromagnetic interference, and generating spectrum difference interference fingerprint data; identifying an uninterfered area according to the spectrum difference interference fingerprint data to obtain spectrum reliability distribution data; extracting non-interfered spectrum section information based on the spectrum reliability distribution data; performing complementary fusion on the non-interfered spectrum section information and the interfered area to obtain texture enhanced video data; and analyzing the time-varying characteristic of electromagnetic interference by using the texture enhanced video data to obtain interference time sequence characteristic data. According to the invention, dynamic calibration of interference is realized by analyzing the deformation characteristic of the calibration frame, and the adaptive capacity of the system to a changing electromagnetic environment is remarkably improved.

The invention discloses a two-way visual saliency detection method and device combining difference guidance and texture enhancement, and the method comprises the following steps: 1, obtaining an image to be subjected to saliency detection, and carrying out the marking and preprocessing of a data set; 2, constructing a visual saliency detection model which comprises a dual-path encoder (a saliency detection path and an image reconstruction path), an adaptive interaction network, a decoder network and an output network; a significance detection path in the dual-path encoder network uses a pre-trained ConvNeXt encoder, and an image reconstruction path uses VQ-VAE as a backbone network; the adaptive interactive network comprises a multi-scale convolution module and a gating fusion module; the decoder network comprises a mutual conversion attention module and a double-gating fusion module; the output network comprises a multi-level feature fusion module; 3, training the saliency detection model to obtain a trained saliency detection model; and 4, carrying out saliency detection on the image data by adopting the trained saliency detection model.

The invention relates to the technical field of smartphone surface defect detection, and discloses a smartphone defect detection method and system, and the method comprises the steps: firstly collecting defect detection image data through multi-dimensional optical imaging, and dividing the defect detection image data into a plurality of detection sub-regions; extracting surface defect features based on a texture enhancement technology, and extracting defect difference indexes in combination with pixel distribution and spatial correlation; then, the regularity of defect modes is analyzed and evaluated through multi-scale decomposition, the defect fluctuation amplitude is dynamically analyzed and evaluated through a sequential sequence, and the stability of defect difference indexes is judged by integrating the regularity and the fluctuation amplitude; and finally, comparing the stable defect difference index with reference data, and determining a detection result. According to the method, through multi-dimensional imaging, multi-scale analysis and dynamic evaluation, the recognition precision and reliability of complex surface defects are improved, the method is suitable for surface detection of smart phones of different materials and structures, and the high-quality detection requirement in large-scale production is met.

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 embodiment of the invention discloses a three-dimensional object texture enhancement method based on coordinate mapping and super-resolution. A specific embodiment of the method comprises the steps of obtaining a scene image data sequence corresponding to a target scene; constructing a three-dimensional scene grid model; generating a scene texture expansion view and image data of each model; the following training steps are executed: obtaining a high-resolution texture expansion graph; generating image data of each rendering model; generating a pixel loss value and a perception loss value; generating an adversarial loss value; generating an image loss value; in response to determining that the image loss value does not meet the preset loss condition, adjusting the parameters of the initial neural network, and executing the training step again; in response to determining that the image loss value meets the loss condition, determining a generative network in the initial neural network as a generative model; and generating a target three-dimensional scene grid model. According to the embodiment, enough training data can be provided for training of the generative network, and the precision and accuracy of the generated three-dimensional grid model can be improved.

The invention discloses a multi-scale frequency-space fusion camouflage target detection method and system, and belongs to the technical field of camouflage target detection, an image and wavelet transform of the image are input into a WaveCamoNet model for camouflage target detection, and the model comprises a double-flow feature extraction module, a cross-domain dynamic fusion module, an edge texture enhancement module and a hierarchical decoder; inputting the image and the wavelet transform of the image into a double-flow feature extraction module, and extracting multi-scale spatial domain features and frequency domain features; inputting the multi-scale spatial domain features and the frequency domain features into a cross-domain dynamic fusion module, and performing multi-scale fusion and cross-domain dynamic fusion to obtain multi-scale fusion features and cross-domain dynamic fusion features; inputting the cross-domain dynamic fusion features into an edge texture enhancement module, and performing spatial calibration to obtain edge enhancement features; and inputting the multi-scale fusion features, the cross-domain dynamic fusion features and the edge enhancement features into a hierarchical decoder for decoding to obtain a binary detection image, and completing pixel-level positioning of the camouflage target.

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 relates to the technical field of image processing, in particular to an underwater crack segmentation-oriented color correction and texture sharpening double-branch enhancement system, which is characterized in that a data set is used for training, reasoning and analysis, and underwater crack images are stored in the data set; the system comprises an input unit used for receiving an underwater crack image; the color correction network is used for carrying out color correction on the underwater crack image and eliminating the problems of color deviation and low contrast of an underwater environment; the texture enhancement network is used for performing texture enhancement on the image after color correction; and the output unit is used for outputting the image enhanced by the texture enhancement network, and aims to recover the color and texture features of the underwater crack image through the color correction network and the texture enhancement network so as to enhance the perceptibility of the semantic segmentation model to the underwater crack.

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 image data processing, in particular to an image adaptive optimization processing method and system for laser printing output, and the method comprises the steps: carrying out the multi-scale feature analysis and fusion of an input original scanning image, and obtaining a fused feature distribution mapping matrix; performing adaptive contrast enhancement based on the fused feature distribution mapping matrix to obtain an enhanced contrast image; performing edge feature extraction on the enhanced contrast image by using an improved multi-direction edge detection algorithm to obtain an edge feature image with an enhanced edge; performing adaptive local texture analysis and enhancement on the edge feature image to obtain a texture enhanced image; and carrying out printing adaptability optimization based on the texture enhanced image to obtain a final optimized output image. According to the technical scheme, the quality of the image printed and output by the laser printing equipment is comprehensively and remarkably improved from multiple image processing dimensions.

The invention provides a method and a system for reconstructing a high-precision complete shape and texture of an object in a scene. The method comprises the following steps: acquiring a visual three-dimensional grid G, optimizing the visual three-dimensional grid G, performing visual modeling of a three-dimensional scene space, and acquiring a three-dimensional grid optimization visual model; through decomposition type nerve implicit reconstruction of the generation diffusion model prior, the potential generation diffusion model prior is used for filling object missing information of an object invisible area or an occluded area, and combined scene reconstruction is carried out; optimizing a visibility model through a three-dimensional grid, dynamically adjusting the prior loss weight of a potential generation diffusion model, forming a generation prior consistent with the input in a visible region, and performing object shape enhancement; and after the object shape enhancement is completed, three-dimensional grid representation is exported from the implicit field, object texture enhancement is carried out, and a high-precision complete object 3D model with complete texture of each object is precisely reconstructed.

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 discloses a digital human voice lip synchronization method based on an adversarial texture enhancement network. The method comprises the following steps: acquiring input audio and video data, extracting audio features by adopting a Wav2Lip model, and realizing preliminary audio and video alignment; performing dynamic modeling on the extracted lip key point sequence by adopting a graph neural network (GCN) so as to capture the time consistency of lip movement; performing texture detail enhancement on the lip region image by using a generative adversarial network (GAN), and introducing an edge perception loss function to optimize the contour definition of the lip region; through a multi-modal fusion strategy, audio and visual features are subjected to joint alignment, and the accuracy of sound and lip synchronization is enhanced; and constructing an audio and video feature consistency evaluation module, and judging and optimizing the generated lip image region. According to the method, the authenticity of the lip movement of the digital human is improved, so that the lip movement better conforms to a real voice expression mode, and the method can be widely applied to the fields of virtual human video generation, film and television post-processing, game role animation, intelligent customer service and the like.

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.

A method for lightweight image restoration is provided. The method includes receiving an input image of a scene captured at a pre-defined zoom level by an imaging sensor of the electronic device; inputting the input image into a naturalness restoration model to obtain a naturalness restored image and restored natural characteristics of the scene; inputting the input image into a texture enhancement model to obtain a texture enhanced image and enhanced texture characteristics of the scene; inputting the restored natural characteristics of the scene, the enhanced texture characteristics of the scene, and the input image into an image restoration model to obtain an intermediate enhanced image corresponding to the input image; and generating, using a fusing unit, an output image that is an enhanced version of the input image based on the intermediate enhanced image, the naturalness restored image, and the texture enhanced image.

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, a system and equipment for enhancing an abnormal scene in a safety limit of an unmanned subway train and a medium, and relates to the technical field of safe driving. The method comprises the following steps: firstly, constructing a pre-trained basic diffusion model, and generating initial subway abnormal scene video data according to an initial frame image and a text prompt; then, a texture enhancement module is used for reducing the texture contrast and improving the trueness of the video; a reference pool containing a real luggage case image is introduced, luggage features are extracted through an object enhancement module, cosine similarity with reference pool features is calculated to serve as a target loss function, and image details are enhanced. And finally, constructing an orbit segmentation algorithm and training the model to realize multi-dimensional enhancement of the abnormal scene. According to the method, an abnormal data scene is generated by means of a diffusion model, and the problem of insufficient training data in subway abnormal object detection is solved through data enhancement.

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 relates to the technical field of lightweight rendering, in particular to a cloud-edge coordinated point cloud model lightweight rendering processing method and system. The method comprises the following steps: performing point cloud coordination prior processing according to electric power facility point cloud data to generate coordination prior point cloud data; a point cloud rendering model is constructed, point cloud curved surface coefficient calculation and curved surface reconstruction processing are carried out, and a point cloud curved surface reconstruction rendering model is generated; obtaining image view angle data, performing rendering model pose alignment processing and model texture enhancement mapping processing, and generating an enhanced texture rendering model; performing rendering coordination lightweight deployment and lightweight real-time rendering processing on the enhanced texture rendering model to generate lightweight real-time rendering data; and obtaining user behavior data, performing point cloud data rendering feedback analysis and cloud edge coordination caching in combination with the lightweight real-time rendering data, and feeding back to the terminal. According to the method, rendering of point cloud power equipment data curved surface reconstruction can be lightened, and redundant transmission of cloud edge rendering data is reduced and coordinated.

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 invention provides an end-to-end lightweight underwater target detection method and system, and belongs to the field of target detection, and the method comprises the steps: carrying out the frame sampling of an underwater video stream at a fixed interval, and obtaining an original image as a to-be-detected image; sending the original image into a differentiable brightness enhancement module and a lightweight texture enhancement module in parallel to generate a color correction image and a texture enhancement image in sequence, and inputting the original image, the color correction image and the texture enhancement image into a space correlation gating attention fusion module to obtain a fusion enhancement image; inputting the fusion enhancement graph into a backbone network formed by connecting a plurality of feature extraction layers in series and then connecting the feature extraction layers in series with a spatial pyramid pooling layer, and outputting a multi-scale feature graph; sending the multi-scale feature map into a path aggregation network to obtain a fused feature map with consistent semantics; and inputting the fusion feature map and the output of the spatial pyramid pooling layer into a multi-branch detection head, completing category, bounding box and confidence prediction, and outputting a final detection result through non-maximum suppression.

The invention discloses a concrete defect detection method and device, electronic equipment and a storage medium, the concrete defect detection method comprises the following steps: in response to a received detection instruction, obtaining a current concrete image of concrete, the detection instruction being used for indicating defect detection on the concrete; the current concrete image is input to a target defect detection model, a corresponding defect detection result is obtained, the target defect detection model is obtained based on a single-step multi-frame target detection model and a texture enhancement model, and the texture enhancement model is used for enhancing and extracting texture features of the current concrete image. According to the method, the target defect detection model constructed based on the single-step multi-frame target detection model and the texture enhancement model is used for detecting the defects of the concrete, the detection time is short, and the detection efficiency and the detection accuracy of detecting the defects of the concrete are improved.

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 relates to the technical field of artwork identification, and discloses a multi-modal artwork fingerprint authenticity identification system based on A1 and a block chain, and the system comprises a multi-modal data collection module which is used for collecting multi-spectral sensing data of artworks, including multi-modal information such as a visible light image, an infrared spectrum image, an ultraviolet spectrum image and a depth image; the preprocessing module is used for preprocessing the collected multi-modal data, including data alignment, denoising, texture enhancement and other operations, so as to improve the data quality and the accuracy of feature extraction; the feature extraction module is used for extracting fingerprint features of artworks, including multi-modal feature information such as surface texture features, material features and shape features, based on a convolutional neural network of a deep learning algorithm, and constructing a dynamic feature template; by innovatively fusing multi-modal data acquisition and a deep learning algorithm and cooperating with an efficient twin network comparison algorithm, the identification accuracy is up to 99.2%, and the accuracy of artwork authenticity identification is greatly improved.

A method for lightweight image restoration is provided. The method includes receiving an input image of a scene captured at a pre-defined zoom level by an imaging sensor of the electronic device; inputting the input image into a naturalness restoration model to obtain a naturalness restored image and restored natural characteristics of the scene; inputting the input image into a texture enhancement model to obtain a texture enhanced image and enhanced texture characteristics of the scene; inputting the restored natural characteristics of the scene, the enhanced texture characteristics of the scene, and the input image into an image restoration model to obtain an intermediate enhanced image corresponding to the input image; and generating, using a fusion unit, an output image that is an enhanced version of the input image based on the intermediate enhanced image, the naturalness restored image, and the texture enhanced image.

Invention Name: A method and system for edge-enhanced remote sensing image segmentation that integrates attention and spatial state models Abstract: The present application discloses a method and system for edge-enhanced remote sensing image segmentation that integrates attention and spatial state models. The implementation steps are: constructing an edge texture feature enhancement structure; introducing the edge texture enhancement structure into the SegNext semantic segmentation model; dividing the remote sensing image segmentation dataset to generate a training sample set, a verification sample set, and a test sample set; preprocessing the dataset; using a neural network to preliminarily extract fine features of the optical remote sensing image, and then training the model with an edge texture enhancement decoder of the channel attention and spatial state model; finally, sending the test sample data to the edge texture enhancement model of the trained attention and spatial state model to obtain the test results. The patent of this invention utilizes the constructed edge texture feature enhancement module and the SegNext semantic segmentation model for collaborative training, which enhances the edge texture features while ensuring the features of the ground objects, thereby improving the accuracy of segmentation.

The invention provides an electronic license anti-counterfeiting storage verification method based on OCR and microscopic texture feature fusion. According to the method, OCR text semantic recognition and physical feature extraction are combined, and the unicity defect that a traditional pure OCR technology is prone to being attacked by content tampering and a pure Hash technology cannot recognize semantic tampering is effectively overcome by binding text content with non-replicable physical features such as paper fiber distribution and ink dot diffusion. In the feature extraction stage, a dynamic semantic alignment module is designed, high-value semantic areas such as identity card numbers and signatures can be synchronously marked in the OCR text positioning process, the texture Hash algorithm is guided to preferentially process the high-risk areas accordingly, the protection strength of key information is improved, the multi-scale texture enhanced Hash algorithm is proposed, and the accuracy of feature extraction is improved. By fusing the advantages of frequency domain wavelet energy analysis and spatial domain LBP feature extraction, even under the condition of low resolution caused by mobile phone shooting, anti-degradation extraction of texture features can still be realized, and the accuracy of counterfeit detection is improved.