64 results about "Texture reconstruction" patented technology

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention relates to the technical field of image compensation, in particular to a reflection compensation method based on image processing, and provides the following scheme: acquiring an environment panoramic image by using a panoramic camera to establish a scene global coordinate system, and acquiring a multi-view original image in combination with a camera array arranged in the circumferential direction of an object; determining a rotation symmetry axis according to the multi-view contour features, reconstructing a three-dimensional geometric body, and calculating normal distribution and curvature change; generating a prediction image based on diffuse reflection and specular reflection hypothesis in the surface expansion domain through virtual visual angle disturbance, identifying a reflection region according to color and gradient consistency, and generating a reflection mask; and performing texture reconstruction on the reflective area by using geometric registration and multi-view compensation, and finally performing splicing and fusion to obtain a non-reflective high-fidelity panoramic image. The method does not need to change the field illumination condition, and can achieve the precise recognition and compensation of the complex curved surface reflection in the cultural relic in-situ collection environment.

The invention relates to a multi-contrast magnetic resonance image super-resolution reconstruction method and system. The method comprises the following steps: collecting an image data set, preprocessing the image data set, and dividing the image data set into a training set and a test data set; combining a Hilbert curve, a state space model and a frequency domain enhancement mechanism to construct a multi-contrast magnetic resonance super-resolution reconstruction model based on Hilbert double-domain fusion Mamba; and training a multi-contrast magnetic resonance super-resolution reconstruction model based on Hilbert double-domain fusion Mamba by using the training data set, and then completing the test of the test data set to obtain a super-resolution reconstruction image of the target contrast magnetic resonance image. Frequency domain features are scanned through a Hilbert curve, a cross-modal global frequency dependency relationship is captured, and high-frequency texture details are effectively recovered. And multi-modal local texture information is dynamically fused through a channel attention mechanism, so that neglect of local details by linear scanning is avoided, and the texture reconstruction precision is improved.

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The invention belongs to the technical field of image enhancement and recognition, and particularly relates to a radar image intelligent enhancement and recognition method and system based on multi-model fusion, and the method comprises the steps: carrying out the adaptive suppression of speckle noise of an original radar image; feature point detection is carried out, robust transformation matrix estimation and adaptive contrast enhancement are carried out, and a corrected and enhanced image is output; utilizing the generative adversarial network and multi-loss function collaborative constraint to obtain a texture reconstruction image; establishing an image-semantic double-flow network architecture, performing cross-modal attention fusion to obtain a fusion feature map, and outputting a target recognition result; performing time phase division on the texture reconstruction image, judging a change type, and outputting a change detection result; and outputting a processing report including the enhanced image, the target list and change analysis. According to the method, noise suppression, correction enhancement, texture reconstruction, target recognition and change detection are integrated, the defect of fragmentation processing in the traditional technology is overcome, and the overall processing performance and the actual application adaptability are improved.

The invention relates to the technical field of image processing and cultural relic protection, in particular to a cultural relic image virtual restoration method based on texture reconstruction and color correction, and the method comprises the following steps: S1, obtaining a digital image of the surface of a to-be-restored cultural relic, and carrying out the denoising and brightness equalization processing of the digital image; s2, identifying and segmenting a damaged area in the image; s3, selecting an optimal sample block which is most matched with the structure of the region to be filled; s4, performing adaptive affine transformation on the optimal sample block to generate a reconstructed texture block; s5, carrying out linear transformation to obtain a repaired texture block after color correction; and S6, seamlessly embedding the repaired texture block after color correction into the damaged area. According to the method, the texture direction alignment and the color distribution correction are combined, so that the consistency of the damaged area and the surrounding image in structure and color is realized, and the naturalness and integrity of cultural relic image restoration are remarkably improved.

The invention provides a method for rapidly detecting the processing degree of rhizoma pinellinae praeparata, and the method comprises the steps: calculating a wrinkle intensity feature from a preprocessed image based on a gray-level co-occurrence matrix, and determining the type of the processing degree of rhizoma pinellinae praeparata by analyzing the wrinkle intensity change of texture distribution in an evolution process from disorder to order; the method comprises the following steps: acquiring surface texture reconstruction data of a plurality of rhizoma pinellinae praeparata samples at each time node under different processing conditions, and establishing a corresponding relationship between image features and processing degree categories; according to the rhizoma pinellinae praeparata processing degree evaluation result, the temperature condition is adjusted, rhizoma pinellinae praeparata image data are collected again, the adjusted wrinkle density feature is extracted, and the rhizoma pinellinae praeparata dynamic change trend is obtained; and by comparing the adjusted rhizoma pinellinae praeparata dynamic change trend with the initial rhizoma pinellinae praeparata dynamic change trend, the processing quality control consistency is judged, and rapid detection of the rhizoma pinellinae praeparata processing degree is completed.

The invention relates to the technical field of video generation, in particular to a video frame generation system and display equipment based on texture reconstruction and trajectory optimization, and the system comprises a motion breakpoint detection module, a trajectory interpolation frame generation module, a texture anomaly positioning module, a texture interpolation reconstruction module and a video frame synthesis module. According to the method, an edge feature map is generated through edge detection and non-maximum suppression, the boundary recognition precision is improved, noise is suppressed, candidate regions are divided through region growth, the local feature extraction efficiency is enhanced, global redundancy is reduced, three-frame optical flow analysis captures trajectory breakpoints, and motion vector correction path deviation is predicted in combination with a Lucas-Kanade method. Color consistency verification optimizes interpolation to reduce cross-frame abrupt change; 64 * 64 grids and a gray level co-occurrence matrix locate texture abnormity; five-frame similarity strengthens time sequence stability; Gabor filtering extracts a main direction and combines an adjacent frame reconstruction texture direction to ensure consistency; and trajectory smoothness and visual consistency are optimized through multi-frame cooperation and layered detection.

The invention belongs to the technical field of image analysis and processing, and discloses a woven fabric texture reconstruction method based on deep shared convolutional dictionary learning, and the method specifically comprises the steps: (1) constructing a deep shared convolutional dictionary learning model; (2) decomposing an overall optimization problem of the constructed deep shared convolutional dictionary learning model into N independent optimization problems; (3) rewriting the N independent optimization problems into three sub-optimization problems with constraints, and converting the three sub-optimization problems into optimization problems without constraints according to an alternating direction multiplier method; (4) inputting woven fabric texture sample images in the training set, and iteratively solving an optimization problem without constraints by adopting a multi-stage training strategy; and (5) inputting the test set image into the deep shared convolutional dictionary learning model, outputting the corresponding adaptive convolutional dictionary and convolutional coding coefficient, and finally calculating to obtain a reconstructed image. According to the method, high-quality reconstruction of a complex woven fabric texture image and general representation of an untrained woven fabric texture image are realized.

The invention relates to the technical field of visual defect detection, in particular to an intelligent dam surface defect detection method based on computer vision, which comprises the following steps: S1, acquiring a dam surface image sequence to form an original detection image set; s2, performing adaptive light field compensation and noise suppression processing on the original detection image set; s3, multi-scale region segmentation is carried out, curvature features are extracted, and a structural feature map is generated; s4, executing three-dimensional texture reconstruction on the basis of the structural feature map, and extracting a surface topography feature vector; s5, defect feature coupling analysis is carried out, and a defect classification matrix is generated; and S6, performing coordinate mapping in combination with a geographic information system, and outputting a defect detection report containing space coordinate information. According to the method, through fusion of multispectral imaging, three-dimensional texture reconstruction and geographic coordinate mapping technologies, high-precision identification and spatial positioning of dam surface defects are realized, and the detection accuracy and the operation and maintenance response efficiency are remarkably improved.

The invention discloses a multi-view texture remodeling method and system based on structure perception, and belongs to the field of computer vision processing. According to the method, a pre-training diffusion model combined with a control network is used for processing a multi-view rendering image of a three-dimensional scene, the image and text description are coded, and structural information is extracted and transmitted to a UNet. Constructing a reconstruction and editing dual-path architecture, and carrying out multi-step iterative denoising on sampled noise; the noise sharing module carries out weighted mixing on the prediction noise of the same batch, the double-flow residual guiding module corrects the prediction noise of different batches, and finally, the three-dimensional original scene is optimized by utilizing the edited image, so that multi-view consistent texture remodeling is realized. According to the method, the super-strong generalization ability of the diffusion model is fully exerted, the texture structure consistency among multiple view angles in the diffusion generation process is improved by combining structure perception guidance and a multi-stage consistency modeling mechanism, and the highly-vivid texture reconstruction ability and the consistent editing performance are shown in the three-dimensional scene editing task.

The present application relates to the technical field of inventory management, in particular to a stone surface intelligent warehouse management method based on machine vision, comprising the following steps: multi-modal sensor acquisition stone image temperature distance data, gray quantization and time difference feature set generation, synchronous calibration, Kalman filter fusion and dynamic weight adjustment, output positioning result and analysis of reflectance coefficient adjustment light source, optimization of light field parameter collection texture reconstruction three-dimensional model and error correction update, based on model extraction position information correction inventory strategy generates inventory management scheme, in the present application, through multi-source sensor cooperative acquisition fusion, positioning consistency is kept, error diffusion is avoided, illumination monitoring and reflection adjustment optimize light field, ensure stone texture complete and stable, three-dimensional model dynamic reconstruction and correction real-time update feature synchronous storage state, improve stacking level and goods location recognition accuracy, enhance inventory information reliability and consistency.

The invention provides a texture map reconstruction method and system and a storage medium, and the method comprises the steps: converting each connected grid in a grid model with an original texture map into a curved surface equivalent to a topological disc, and constructing an optimal transmission mapping for the curved surface of each topological disc based on a discrete optimal transmission theory; and solving the optimal transmission mapping to obtain pixel coordinates under area-preserving parameterization, and performing texture reconstruction by taking the original texture map as a source pixel value according to the pixel coordinates under the area-preserving parameterization to obtain a texture reconstruction image. According to the method, area-preserving parameterization is constructed through an optimal transmission theory, each triangular patch in a network of an original texture map is mapped to a plane parameter domain according to the area, and a texture image is regenerated, so that blank and invalid pixel regions are eliminated, texture details are ensured not to be distorted, and the image quality is improved. The problems of storage space waste and insufficient rendering performance are solved.

The invention discloses a camera image acquisition parameter verification method and system based on big data analysis, and relates to the technical field of image data processing. The camera image acquisition parameter verification method and system based on big data analysis comprises the following steps: S1, preprocessing image equipment data and statistical prior data; s2, completing re-parameterization sampling of potential variables through joint coding of an attention fusion mechanism and a variational auto-encoder; s3, constructing a de-noising diffusion probability model, establishing a forward diffusion sequence and reverse de-noising, and forming a reverse generated image sequence and a generated feature map; s4, discriminating and evaluating the domain consistency of the generated image and the target quality image through an adversarial discrimination network; and S5, uniformly updating the network parameters. The problems that the calculation load is too heavy and the imaging speed is low due to the fact that an existing single-image super-resolution technology is insufficient in high-frequency detail recovery, unreal in texture reconstruction and low in diffusion sampling efficiency are solved.

The application relates to a complex texture textile defect recognition method and system based on feature decoupling, and relates to the field of computer technology, which comprises the following steps: obtaining a multi-scale feature set of an original textile image to obtain a complex texture fusion feature; projecting the complex texture fusion feature to two mutually orthogonal hidden subspaces to obtain a reference texture component and an abnormal disturbance component; performing manifold alignment processing on the reference texture component based on a preset texture library to obtain a texture reconstruction feature; performing feature fusion on the texture reconstruction feature and the abnormal disturbance component to obtain a texture residual feature; performing adaptive threshold segmentation on the texture residual feature to obtain a defect feature map of the original textile; and performing connected domain analysis processing on the defect feature map to output a defect detection result. The application has the effects of improving the robustness of textile detection, meeting the needs of real-time detection in a pipeline, and reducing the computing resources and labor costs.

The application discloses an image compression sensing method of domain sampling and mixed transformer, comprising the following steps: a sampling recovery stage: learning the deep representation of the input image through multi-layer progressive 5*5 and 7*7 convolution and nonlinear activation function, and fully interacting the fragmented window information, and then generating the fine reconstruction iteration stage containing low and high level aggregation features at the same time through the jump connection; a fine reconstruction iteration stage: using a deep gradient descent module to perform optimization update of the reconstruction feature, and the deep gradient descent module expands the calculation of the update fidelity term to the neural network. Through the innovative network architecture and training strategy, the application realizes excellent image reconstruction performance, especially at a low sampling rate, and the method exhibits significant advantages and positive gain effects in detail preservation, texture reconstruction, robustness and universality compared with existing methods, thereby providing strong technical support for research and application in the field of image reconstruction.

The invention relates to the technical field of inventory management, in particular to a stone layout intelligent warehouse management method based on machine vision, which comprises the following steps: acquiring stone image temperature and distance data through multi-modal sensing, generating a feature set through graying quantization and time difference, performing Kalman filtering fusion after synchronous calibration, and dynamically adjusting the weight. And outputting a positioning result, analyzing a reflection coefficient, adjusting a light source, optimizing a light field parameter, collecting a texture reconstruction three-dimensional model, correcting and updating an error, extracting position information based on the model, correcting an inventory strategy, and generating an inventory management scheme. The light field is optimized through illumination monitoring and reflection adjustment to ensure that the stone texture is complete and stable, the feature synchronous storage state is updated in real time through three-dimensional model dynamic reconstruction and correction, the stacking level and goods allocation recognition accuracy is improved, and the inventory information reliability and consistency are enhanced.

This application relates to the field of tunnel construction safety monitoring technology, and discloses an intelligent identification method for water seepage risk at the tunnel face in water-rich clay layers. This method controls an active infrared excitation module to project radiation in an oblique incidence manner, uses polarization imaging to acquire sequences and calculates Stokes vectors; calculates the degree of linear polarization based on the Stokes vectors and performs threshold segmentation to generate a water film distribution mask; uses the Stokes vectors to construct a polarization angle field reflecting local normal changes as texture features, solves the optical flow constraint equation to obtain the instantaneous velocity field; performs frequency domain transformation on the velocity field, extracts the proportion of low-frequency creep energy through power spectral density analysis, and determines the soil rheological softening factor; finally, combines the water film distribution, rheological softening factor, and instantaneous velocity field to construct a mudslide risk index. This invention utilizes polarization texture reconstruction to solve the problem of weak texture tracking and eliminates elastic vibration interference through frequency domain energy decoupling, effectively improving the accuracy of mudslide risk identification.

The invention relates to the technical field of three-dimensional reconstruction, and discloses a face three-dimensional reconstruction method, system and device based on a portable device and a medium, and the method comprises the steps: collecting a face image frame sequence of a target user through the portable device; performing face key point detection on each frame of face image in the face image frame sequence, and performing face geometric reconstruction based on a face key point result in combination with a multi-stage fusion algorithm to obtain a face geometric reconstruction model; determining a plurality of key face images in the face image frame sequence based on a frame selection strategy, performing de-illumination processing on each key face image, and fusing each de-illuminated face texture image through a Laplacian pyramid network to generate a face texture reconstruction model; and mapping the face texture reconstruction model to the face geometric reconstruction model to obtain a complete face three-dimensional reconstruction result. According to the invention, the hardware cost can be reduced, the convenience is improved, remote medical treatment is realized, and the face three-dimensional reconstruction precision and quality are improved.

This application discloses a method and system for detecting defects in metal products based on image recognition. The method first performs reflection perturbation elimination processing on the surface image of the metal product to be detected to obtain a reflection elimination image; then, it acquires the surface reference texture features of the defect-free metal product, and generates a reference texture model based on texture distribution patterns, gray-level mean, and texture continuity; next, it performs defect texture gradient separation on the reflection elimination image based on the reference texture model, locates abnormal regions, and segments them to obtain suspected defect texture regions; then, it performs boundary pixel reconstruction on the suspected defect texture regions to obtain defect texture reconstruction regions; the target defect texture region is obtained through local variance enhancement and neighborhood correlation analysis; finally, it locates overexposed regions and performs gray-level inverse stretching processing, and outputs the surface defect detection results after clustering abnormal pixel group features of the preprocessed defect regions, thereby improving the accuracy and precision of surface defect detection of metal products and reducing the false detection rate.

The application relates to the technical field of virtual reality, in particular to a VR tactile feedback system based on multi-modal perception fusion and a method thereof, which comprises a tactile interaction terminal, a distributed tactile feedback terminal, a distributed signal acquisition unit, a computer system, a tactile presentation device, a visual device and an auditory device; the core of the application is a tactile prediction unit in the distributed tactile feedback terminal; a 3D content tactile prediction algorithm based on matrix theory is constructed; tactile texture matrix and tactile material matrix are used to realize accurate mapping of visual features and tactile features; a high-dimensional matrix is innovatively used to represent a tactile feature space; a matrix spectrum analysis technology is used to realize adaptive adjustment of tactile features; a multi-scale matrix decomposition and fusion algorithm is used to realize tactile texture reconstruction from a macroscopic view to a microscopic view; the application solves the problems of limited tactile feature expression capability, lack of adaptability and insufficient multi-modal perception cooperation in a traditional VR tactile feedback system.

Certain aspects of the present disclosure provide techniques for reconstructing a texel of a texture. Such techniques may include receiving a plurality of sets of features corresponding to the texture, wherein the plurality of sets of features comprises a respective set of features for each respective grid point of a grid; receiving coordinate information corresponding to the texel of the texture; receiving level of detail information; selecting a subset of grid points of the grid based on the second resolution being lower than the first resolution; sampling one or more grid points from among the subset of grid points based on the coordinate information to obtain sampled features associated with the one or more grid points; inputting, to a machine-learning model, the sampled features; and receiving, from the machine-learning model, based on the sampled features, a reconstruction of the texel of the texture at the second resolution.

The present invention relates to the field of model construction technology, and in particular to a method for creating an image recognition model based on clinical disease data integration. The method comprises the following steps: acquiring a medical clinical orthopedic image set; extracting bone density and bone morphology from the medical clinical orthopedic image set and performing bone structure segmentation on the medical clinical orthopedic image set to generate a bone structure segmentation image, wherein the bone structure segmentation image includes a large bone structure image and a small bone structure image; analyzing the bone layer thickness of the large bone structure image and performing physical layer thickness compensation reconstruction on the large bone structure image to generate large bone intermediate virtual slice data; performing detail-guided reconstruction on the small bone structure image to generate small bone fracture texture reconstruction data. The present invention improves the accuracy of dynamic bone image recognition by enhancing image resolution, integrating multimodal data, performing motion simulation, and optimizing model training.

The invention discloses a road modeling system, a repairing spraying action adjusting method and a road repairing robotic.The road modeling system conducts digital modeling on a road environment and crack details by collecting multi-view multi-mode image data of a designated area and combining a collaborative perception mechanism, and the method comprises the steps that S1, quality optimization is conducted on the image data; s2, performing dense three-dimensional texture reconstruction on the image to form a preliminary earth surface model, S3, realizing spatial registration of ground point cloud and aerial survey point cloud for the model, and generating high-density point cloud; and S4, constructing an image-point cloud double-flow feature coding network, and reconstructing to obtain a three-dimensional road crack model. According to the method, road infrastructures can be converted into predictable and controllable digital objects from physical static assets, accurate mapping and virtual-real synchronization of road damage states and robot behaviors are achieved, and the cognitive precision, virtual-real consistency and feedforward decision-making ability of a repair system to complex operation scenes are remarkably improved.

The present application relates to the technical field of image optimization, in particular to a UAV aerial image optimization method and system based on artificial intelligence, comprising the following steps: calling flight state information to analyze attitude deviation, calculating the sun irradiation angle to adjust exposure, evaluating noise indicators to adjust filtering, identifying scene labels to match processing parameters, generating gradient heat maps to optimize texture, and obtaining aerial image optimization records. In the present application, by calling real-time UAV flight attitude data and light change trend, the attitude and light state deviation in the shooting process are finely evaluated, the image geometry and light parameters are precisely adjusted, the filtering strength is dynamically optimized by using the regional noise deviation amplitude, the scene features are constructed according to the local semantic density, the texture reconstruction strength is dynamically classified and optimized according to the local gradient change of the image, the problems of uneven illumination and regional detail loss of aerial images are effectively reduced, and the overall visual clarity and content recognition accuracy of the image are improved.

A facial texture reconstruction unit reconstructs a texture of a face from a 2D video of a person. A facial shape reconstruction unit reconstructs a 3D shape of the face from the 2D video. A pose estimation unit estimates a pose of the person from the 2D video. A shape integration unit reconstructs a 3D shape of the body corresponding to the estimated pose based on the 3D shape data, and integrates the reconstructed 3D shape of the body and the reconstructed 3D shape of the face to reconstruct a 3D shape of the person. A texture reconstruction unit reconstructs a texture image of the person by blending, with an image of the reconstructed texture of the face, a texture image included in the texture data and a model generation unit generates a 3D model of the person based on the 3D shape and the texture image of the person.

The invention provides an image texture defect repairing method, a segmentation method and a program product, and provides a newly designed normal texture reconstruction network for repairing and reconstructing the texture defect of an image, the reconstruction network comprises an encoder, a multi-scale median filtering module and a decoder, the multi-scale median filtering module realizes refined reconstruction and restoration of image texture details through multi-stage feature processing, abnormal information of a defect area can be effectively eliminated, the performance is excellent in normal texture detail restoration, and texture structure continuity, color consistency and marginal definition are greatly improved; and based on the reconstructed image, generating a segmented image by using a comparison network which is newly designed, so that pixel-level detection of the surface texture defects of the industrial product is realized, the defect detection precision is as high as 100%, and the defect positioning precision is not lower than 97.6%.

The invention discloses a human body reconstruction method and system based on a single RGB-D image and side edge optimization, and relates to the field of digital human reconstruction processing. According to the invention, a point cloud initialization module fully utilizes depth conditions to optimize an SMPL-X model so as to construct a back surface depth map and a human body point cloud model, and then a side surface hybrid optimization module combines a front surface RGB image with the optimized SMPL-X model to carry out geometric detail optimization on a side surface so as to obtain a left side depth map and a right side depth map. And finally, a texture generation module is used for realizing high-fidelity texture estimation from the aspects of text and vision through multi-network combination, and then the texture is mapped to the texture-free human body geometric model through UV to obtain a human body geometric model with complete texture. According to the method, high-performance geometric and texture reconstruction of the complete human body geometric model can be realized, and the reconstruction effect is improved.

The application discloses a mesh texture simplification algorithm suitable for three-dimensional reconstruction, and comprises the following steps: S1, reference three-dimensional model scene construction; using the recovered scene structure in three-dimensional reconstruction and the calibrated image, performing texture mapping on the original fine three-dimensional mesh to complete the reconstruction of the reference three-dimensional model scene; S2, reference three-dimensional model scene image acquisition; according to the three-dimensional space relative attitude relationship between the reference three-dimensional model scene and the view internal and external parameters, using the inverse projection principle to perform three-dimensional mesh to two-dimensional image rasterization calculation pixel by pixel and view by view, and completing the acquisition of the reference image set; S3, mesh and texture simplification; using the QEM algorithm to simplify the mesh, using the reference image set as a data source and using the texture reconstruction algorithm to perform texture remapping and simplification. The application has the advantages that the algorithm can support mesh and texture simplification together, under different texture simplification parameters, the texture almost has no distortion and can significantly reduce the texture data amount.

This invention relates to the field of industrial machine vision inspection and image processing technology, specifically to an image enhancement-based system for inspecting the surface finish of automotive component production molds. The system includes a data acquisition module for acquiring the original grayscale image of the automotive component mold surface; performing low-pass filtering on the original grayscale image to eliminate imaging thermal noise; a manifold reconstruction module for constructing a structure tensor field; inversely inferring the pseudo-curvature field of the mold surface; an adaptive enhancement module for generating a correction image; constructing a texture orthogonal anisotropic diffusion model; generating a texture reconstruction reference image; and a surface metrology module for calculating the difference between the correction image and the texture reconstruction reference image, generating a defect saliency map; and calculating the surface roughness value of the mold surface. This invention effectively solves the problem of existing technologies struggling to distinguish between designed textures and abnormal scratches, overcoming the technical bottleneck of traditional visual inspection easily missing minute defects in complex geometric structures.