56 results about "Anisotropic diffusion" patented technology

The invention relates to the technical field of special equipment nondestructive testing image intelligent identification, and discloses a special equipment nondestructive testing image defect automatic identification method and system, and the method comprises the steps: carrying out the preprocessing based on an anisotropic diffusion mechanism; a local self-adaptive threshold method is adopted to complete preliminary defect area positioning; constructing a scale consistency constrained multi-scale image pyramid; constructing defect morphological parameters; and carrying out defect type identification by fusing fuzzy form constraint and an SVM classification mechanism. In the prior art, a global filtering or fixed threshold segmentation method is mostly dependent, and especially under the conditions of V-shaped grooves, multi-layer weld structures and corrosion perforation defects, accurate identification of irregular, multi-scale and weak-contrast defects cannot be realized. According to the method, the anisotropic diffusion mechanism is introduced, the multi-scale response extraction of scale consistency constraint is combined, and the classification strategy of fuzzy form modeling is fused, so that the accuracy of special equipment image defect identification is improved.

The invention discloses a dynamic scene three-dimensional reconstruction method and device based on hydrogen energy unmanned aerial vehicle survey, and the method comprises the steps: obtaining dense time sequence multi-view image data of a target region through a hydrogen energy unmanned aerial vehicle platform, and carrying out the preprocessing of radiation correction and geometric correction; carrying out optical flow analysis and deformation rate clustering on the preprocessed image, identifying a pseudo-static anchor point and constructing a dynamic reference field; introducing a dynamic reference field as a soft constraint in a binding adjustment process, and optimizing a camera pose to generate a three-dimensional point cloud with consistent time and space; and finally, mapping the point cloud to a space-time voxel grid, constructing a surface evolution model by using a graph neural network or an anisotropic diffusion algorithm, and calculating a surface deformation vector to realize continuous and high-precision three-dimensional reconstruction of the disaster scene surface deformation process.

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 belongs to the technical field of image processing, and particularly relates to an oil and gas engineering supporting facility intelligent detection method based on machine vision, and the method comprises the steps: constructing a structure tensor matrix based on the gradient of pixel points, calculating a local linear structure response value according to two feature values of the structure tensor matrix, and calculating a local linear structure response value; according to the direction alignment degree of the feature vectors of the pixel points on the local path, the crack continuity of the pixel points is calculated, the morphological significance values of the pixel points are comprehensively obtained and used for adjusting basic contrast parameters, anisotropic diffusion processing is carried out on the to-be-detected image of the oil and gas facility based on the obtained self-adaptive contrast parameters, and the to-be-detected image of the oil and gas facility is obtained. An enhanced image is obtained through multiple times of iterative updating, and a defect detection result graph is obtained through edge detection. According to the method, the problem that false defects are easily misjudged as cracks in a traditional method is solved, and the detection accuracy and reliability are remarkably improved.

The invention provides a thyroid ultrasonic signal intelligent analysis and diagnosis method and system, and relates to the technical field of ultrasonic signal analysis, and the method comprises the steps: calculating different depth noise distribution, automatically adjusting a filtering parameter value, and carrying out the filtering through anisotropic diffusion; segmenting the processed image to obtain a nodule region; acoustic characteristic data, acoustic characteristic parameter data and image characteristic data are extracted to form a comprehensive characteristic vector, and dimension reduction is carried out; and calculating a feature association weight for optimization to obtain a classification result, and generating a diagnosis report. The thyroid ultrasound image analysis precision is improved, and intelligent diagnosis of benign and malignant nodules is achieved.

The invention relates to an SAR image fusion method, device and equipment in a terahertz frequency band bunching mode, and the method comprises the steps: carrying out the preprocessing of a terahertz bunching mode video, and enabling a reference image and a to-be-fused image to be projected to a ground plane through combining the linearity and interpolation of a radar imaging digital elevation model, thereby obtaining a corresponding projection image; constructing an anisotropic diffusion scale space image for the two types of projection images, generating a Harris function scale space image according to the anisotropic diffusion scale space image, extracting extreme points, constructing SIFT feature descriptors of the two types of images based on the extreme points, obtaining an initial feature matching point pair set through similarity comparison, obtaining an optimized point pair set after multiple times of screening, and obtaining an anisotropic diffusion image through the optimized point pair set. And calculating an image conversion affine matrix by least square matching, fusing the to-be-fused image and the reference image according to the matrix to obtain a fused image, updating the image as the reference image, and repeatedly fusing the reference image with the next to-be-fused image until all frames are fused to obtain a final fused image. By adopting the method, the quality of the fused image can be effectively improved.

The invention discloses a display method and system of an electronic rearview mirror based on multi-mode risk field coupling. The display method comprises the steps that driving state data of a current vehicle and one or more target vehicles and observation point data of a current vehicle driver are obtained; constructing a coupling dynamic risk field model based on the driving state data, and generating a comprehensive risk value; generating an attention entropy and a visual search efficiency value based on the observation point data; generating a display control signal by the comprehensive risk value, the attention entropy and the visual search efficiency value through a visual information flow optimizer based on an optimal control theory; and according to the display control signal, simplifying the background region by adopting an anisotropic diffusion equation, enhancing the foreground target by utilizing a nonlinear rendering pipeline, generating an electronic rearview mirror synthetic picture, and displaying the electronic rearview mirror synthetic picture on an in-vehicle display screen. According to the invention, the prominent presentation of key risk information and the effective compression of redundant information are realized, the cognitive load of a driver is reduced, and the driving decision-making efficiency and safety are improved.

The application discloses an underwater polarization imaging method based on Stokes vector anisotropic diffusion, and has the characteristics that the method comprises the following steps: collecting multiple underwater polarization images with different polarization directions; performing brightness normalization processing on the collected underwater polarization images; constructing a Stokes vector based on the brightness normalized underwater polarization images; approximating the diffusion process of the Stokes vector in different regions by calculating the gradient difference of four directions, and estimating the gradient required in different regions according to the diffusion process; introducing a Perona-Malik anisotropic diffusion model, constructing a Stokes vector diffusion model by Euler discrete approximation based on the gradient, and obtaining the diffused Stokes vector; calculating the polarization angle and the degree of polarization according to the diffused Stokes vector; performing depolarization compensation correction on the degree of polarization to obtain the corrected degree of polarization; estimating the backscattering light based on the polarization angle and the corrected degree of polarization; and obtaining the final imaging result based on the estimated backscattering light.

The invention provides a plant leaf included angle measuring method and device. The method comprises the following steps: generating a corresponding depth map according to an obtained plant image, performing normalization processing on the depth map and the plant image, determining a corresponding depth gradient feature according to the normalized depth map, and fusing the normalized depth map, the normalized plant image and the depth gradient feature to obtain fused feature space data; clustering the fused feature space data to determine an accurate initial mask of the foreground; filtering the initial mask by taking the normalized depth map as guidance, retaining main edge features, and performing anisotropic diffusion on the filtered image according to depth gradient features to obtain a smooth image; the skeleton structure of the smooth image is extracted, the trunk branch and the side branch are determined, the trunk straight line and the side straight line are obtained through straight line fitting, the included angle between the trunk straight line and the side straight line is used as the leaf included angle, the method can be suitable for determining the leaf included angles of plants in various different growth stages, and high practicability and adaptability are achieved.

The application provides an electromagnetic coil fault detection system and method based on image analysis. The system obtains a visible light image of the surface of a target electromagnetic coil during operation and segments an initial hot spot area. The system calculates the normal direction of each contour point on the edge contour of the initial hot spot area, performs anisotropic diffusion analysis on the initial hot spot area based on the normal direction of each contour point and the dominant conduction direction of the winding of the target electromagnetic coil on the image plane, and generates a plurality of diffusion state images formed by heat conduction to the surface of the electromagnetic coil. The system performs multi-scale matching of each diffusion state image and the morphological features of the initial hot spot area, selects a target diffusion state image, and identifies the spatial position of a potential heat source fault in the initial hot spot area based on the target diffusion state image and the winding spatial topology model of the target electromagnetic coil. The technical solution provided by the application can identify the thermal fault position of the electromagnetic coil under the anisotropic influence of the winding of the electromagnetic coil.

The present application relates to the field of geophysical exploration technology, and particularly relates to a method for well leakage early warning by using seismic prediction results, comprising: basic data acquisition, regional geological condition analysis, seismic data preprocessing, small fracture and fracture-vug body seismic fine delineation, and shadow relief display of the delineation result. The present application improves the basic data quality by introducing anisotropic diffusion and three-dimensional multi-level median joint filtering technology, and carries out multi-attribute fusion based on adjustable parameters by combining pre-stack multi-azimuth frequency attenuation gradient fracture prediction and post-stack structural coherent tensor, finally realizes spatial display of the multi-attribute fusion result by using shadow relief technology, improves the accuracy of small fracture and fracture-vug body delineation, and more directly shows the spatial form of the fracture and fracture-vug body, which can effectively guide well location design, drilling design and drilling trajectory optimization.

The high-brightness interference-eliminating optical film comprises a diffusion film (500), an upper prism film (100) and a lower prism film (200), a plurality of prism structures (201) arranged in parallel are formed on the upper prism film (100) and the lower prism film (200), the diffusion film (500) at least comprises an anisotropic diffusion layer (501), ellipsoidal PMMA particles (511) are dispersed in the anisotropic diffusion layer (501), and the ellipsoidal PMMA particles (511) are uniformly distributed in the anisotropic diffusion layer (501). The ellipsoidal PMMA particles (511) are oriented and arranged along the stretching direction; the thickness of the anisotropic diffusion layer (501) is 30-60 [mu] m. According to the utility model, through the arrangement of the diffusion film comprising the anisotropic diffusion layer, the high brightness of light transmission can be maintained, the transverse scattering of light can be enhanced under the condition that the brightness is not reduced, the interference is effectively eliminated, and the contradiction that the high brightness and the interference elimination are difficult to harmonize in the prior art is solved.

This invention belongs to the field of image processing technology, specifically relating to an automatic measurement method and system for material quantity in a silo based on machine vision. The method includes: acquiring depth and infrared intensity images of the silo; preprocessing these images to generate a valid binary mask; determining a geometric confidence factor based on the deviation between the depth gradient and the natural angle of repose of the material being measured, as well as local curvature; and determining an environmental confidence factor by combining infrared intensity and measurement distance; repairing invalid pixels using anisotropic diffusion; predicting depth values ​​based on first-order Taylor expansion and multi-dimensional confidence weighting; converting the repaired depth image into physical height and projected area; and obtaining the total volume of material in the silo through infinitesimal element accumulation. This invention solves the problem of data loss in complex environments and improves measurement accuracy.

The invention provides a field core box corner extraction and perspective distortion correction method based on multi-scale Retinex fusion. The field core box corner extraction and perspective distortion correction method comprises the following steps: S1, preprocessing a core box image based on anisotropic diffusion and multi-scale Retinex fusion; s2, core box angular point extraction based on topological constraint Shii-Tomasi and graph cut optimization interaction; s3, perspective correction based on thin plate spline transformation and Markov random field fusion; and S4, performing correction result precision verification and closed-loop feedback based on Hough transform straight line fitting and geometric moment invariant fusion. According to the method, accurate extraction of core box angular points and efficient distortion correction of images are realized, and meanwhile, full-dimension precision verification of correction results, adaptive migration of processing parameters of batch images and lightweight field deployment of algorithms are completed.

The invention discloses an image data enhancement method and system based on insect situation analysis, aims to solve the problems that insect details are fuzzy and structural features are not prominent in an insect situation monitoring image, and relates to the technical field of image processing. The method comprises the following steps: acquiring an original pest image through monitoring equipment, and generating a to-be-enhanced image sequence comprising complete annotation information through size normalization and color space conversion; based on multi-scale anisotropic diffusion, adaptively decomposing the image into a structure substrate layer and a texture detail layer, and realizing feature separation through grid partition threshold calculation, dynamic diffusion coefficient adjustment and decoupling quality index quantitative evaluation; carrying out global energy-driven adaptive high-frequency enhancement and iterative optimization on the texture detail layer, and carrying out regional perception contrast enhancement and edge sharpening on the structure substrate layer; and the enhanced layered images are fused and recombined into RGB images, and a closed-loop quality evaluation system is constructed in combination with the information entropy and the peak signal-to-noise ratio, so that the detail visibility and the structural integrity of the insect body image are effectively improved.

The application relates to the technical field of medical ultrasonic imaging and digital image processing, in particular to an ultrasonic elasticity image denoising and enhancing method, which comprises the following steps: acquiring original ultrasonic elasticity imaging data to be processed, and constructing a structure tensor matrix reflecting local texture directions; solving the anisotropic coherence degree of each pixel point, obtaining a structure attribute discrimination result, and constructing a structure confidence atlas; collecting the point spread function characteristics of an ultrasonic imaging system, establishing a speckle noise statistical model; obtaining a local smoothing coefficient of the first iteration, and performing anisotropic diffusion correction on an initial elasticity distribution matrix to obtain a first modified elasticity image matrix, and updating the structure confidence atlas to obtain a final denoised and enhanced target elasticity image; the application effectively suppresses multiplicative granular speckles, eliminates the ladder effect or artifacts easily generated by conventional methods, and significantly improves the image signal-to-noise ratio.

This invention discloses a method for intraoperative navigation of craniostomy based on curvature guidance and online pose maintenance, belonging to the field of intraoperative navigation of craniostomy. This invention constrains the curvature prior of the natural human structure: curvature estimation uses the auricle (along the arcuate ridge line of the helix / antihelix) and the nasal ala (along the arcuate ridge line of the nasal ala rim / nasal groove) as anatomical references, preferentially preserving these curvature features and suppressing their tangential "sawtooth" jitter; it provides a continuous and stable 6DoF initial pose on the time axis, and gating freeze / backoff is used to avoid initial value jumps in case of occlusion or mismatch; spatially, curvature-guided filtering is used, combined with anisotropic diffusion of prior constraints to complete edges and holes, improving effective overlap and connectivity. The correspondence between point clouds is more reliable, and the initial values ​​are closer to the true solution, thus enabling robust kernel ICP to converge stably under normal / distance gating, meeting the requirements of high-precision rigid registration and continuous navigation of intraoperative-preoperative 3D models.

The invention discloses a cross-modal target detection system and method, and relates to the technical field of computer vision, and the method comprises the steps: collecting a visible light image and a point cloud, carrying out the calibration of an internal reference and an external reference, projecting the point cloud to a pixel grid to generate a depth map, calculating the gradient of the visible light image, the gradient of the depth map and the point density of the point cloud, and constructing a geometric reference domain. Bidirectional mapping is obtained; according to a geometric reference domain and bidirectional mapping, forming a general bottom layer feature, obtaining a two-mode feature graph through lightweight adaptation, and generating a pixel-level credibility graph; determining a dominant mode according to the pixel-level credibility graph, constructing an initial fusion field, constraining a propagation boundary through a geometric reference domain, executing edge preserving linear fusion and anisotropic diffusion, and forming a fusion feature pyramid; and generating anchor points on the fusion feature pyramid according to the fusion features, the geometric reference domain and the pixel-level credibility map, generating a target candidate frame, performing boundary correction, and outputting the target position and category. And multi-scale positioning stability is realized.

The present application relates to the technical field of single crystal silicon wafer defect detection, more specifically, to a product single crystal defect locking system and method based on the principle of black cross light disappearance of silicon wafer, which is used to solve the problem that the prior art usually lacks effective verification of the mechanical orthogonal relationship between the fracture characteristics and the local stress direction, and only relies on image intensity or simple threshold to extract the fracture endpoint, which is easy to be interfered by noise, imaging non-uniformity or crystal microstructure, resulting in false fracture misjudgment; the present application fuses polarization response and mechanical priori through a black cross fracture stress verification module, positions the fracture endpoint by modulating the consistency of depth and phase, combines weighted robust fitting to inverse the local stress direction, and strictly verifies the fracture-stress vertical relationship by statistical test, finally takes the verified points as the boundary to drive anisotropic diffusion to generate a physically reasonable, noise-robust and full-field guided feature field, which provides reliable priori for high-precision stress reconstruction.

This invention discloses a three-dimensional measurement method for micro / nano devices based on an improved focusing evaluation function. The method includes the following steps: processing acquired sequential images, calculating a structure fidelity index, and assembling the processed images into a three-dimensional image matrix; generating gradient response maps based on sliced ​​images in the three-dimensional image matrix, combining the response maps with the structure fidelity index and a noise reduction term to construct an improved focusing evaluation function, obtaining a focusing value sequence; performing a preliminary peak search on the focusing value sequence, constructing a candidate dataset, and calculating a peak quality factor; calculating the comprehensive effective quality based on the selected candidate fitting dataset, and solving for the final depth coordinates of the pixels based on the effective quality; generating an initial sparse point cloud, and using the point cloud to generate a surface consistency index, driving an anisotropic diffusion algorithm and depth iteration, outputting a three-dimensional morphology model and size parameters. This invention improves the depth measurement accuracy and edge details of three-dimensional reconstruction of micro / nano devices.

The invention provides a carotid artery blood flow core temperature measuring method and system based on spectrum recognition. The method comprises the steps that ultrasonic image data and multi-wavelength heat radiation temperature time sequence data in a carotid artery area are collected; outputting a smooth multi-wavelength temperature time sequence data set; rigidly registering the ultrasonic image data; performing anisotropic diffusion processing on the registered image, and outputting a multi-modal fusion image; detecting a thermal anomaly region in the fused image by setting a local anomaly factor, and outputting a sensitive feature vector; and adopting empirical mode decomposition to eliminate respiration interference components in the sensitive feature vectors, and outputting clinical intervention suggestions. According to the method, the reference frame image and the current blood vessel frame image are input into the deep learning model AlexNet, the multi-scale feature map is extracted, the global displacement is calculated through the absolute average difference to determine the rough matching displacement, then the sub-pixel displacement is calculated through bilinear interpolation, the problem of non-rigid deformation of the blood vessel is effectively solved, and the accuracy and reliability of blood vessel feature extraction are improved.

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.

The invention discloses a dam engineering leakage channel entrance-channel-escape full view identification method and system. The method comprises the following steps: acquiring multi-source monitoring data of a dam engineering area; constructing a background seepage flow field reflecting a macroscopic hydraulic gradient and an anisotropic diffusion tensor field reflecting local permeability characteristics based on multi-source monitoring data; performing bidirectional probability field evolution calculation by using an unsteady convection-diffusion equation set and respectively taking the inlet geometrical characteristics and the escape temperature anomaly as sources to obtain an inlet reachability field and an escape reachability field which are continuously distributed in the dam body space; and determining the three-dimensional full-view probability distribution of the leakage channel based on the space interaction overlapping characteristics of the two fields. According to the method, physical self-consistent reconstruction from discrete monitoring data to a continuous channel entity is realized by constructing a different-field coupling feedback and tensor guiding mechanism, and the topological fuzzy problem of leakage path identification in a complex medium is effectively solved.

The invention discloses a flow field gradient determination method and device based on iterative diffusion, equipment, a medium and a product, and belongs to the technical field of numerical simulation. The method comprises the following steps: dividing a flow field area into a shock wave area and a smooth area; prior units are selected from the shock wave area and the smooth area, and gradient values of the prior units are calculated; marking the gradient value of the prior unit as a frozen state as a reference value of neighborhood diffusion so as to iteratively update the gradient value of the non-prior unit; in the diffusion process, based on the geometrical relationship between the non-priori unit and each adjacent unit, the anisotropic diffusion weight between the non-priori unit and each adjacent unit is calculated; iteratively updating the gradient value of the non-prior unit by using the diffusion weight and the gradient value of each adjacent unit until a convergence condition is reached; and determining the gradient value of each unit after convergence as the flow field gradient. The precision and stability of gradient calculation can be improved.

The present application relates to the field of image processing, and specifically discloses a food production line real-time monitoring method and system based on image processing, comprising the following steps: S1, obtaining the original image of the food to be monitored, and obtaining the pretreated image after gray scale and smoothing by using the anisotropic diffusion model with the diffusion coefficient being inversely proportional to the gradient amplitude; S2, calculating the gradient amplitude and gradient direction of each pixel point in the pretreated image; S3, for any pixel point in the pretreated image, calculating the fractal dimension and local phase consistency of the window region of the pixel point, and based on the two characteristic values, obtaining an adjustment coefficient from the preset lookup table, and adjusting the preset reference threshold value by using the adjustment coefficient to obtain a dynamic high threshold T high The present application has the beneficial effect that the basis for judging edge connection is more sufficient, and a more complete, continuous and smooth food contour can be generated, and the contour breakage and burr are significantly reduced.

The application relates to the field of building information model application technology and engineering project management technology, and discloses a building engineering progress intelligent prediction system based on BIM, which firstly analyzes BIM and progress data, constructs a semantic constraint topological graph containing logical dependence and spatial proximity relationship, then discretizes the building space, constructs an anisotropic diffusion tensor based on component semantics, realizes physical tensorization of geometric constraints, on the basis of which, the system performs multi-physical field coupling calculation, solves the distribution of material flow resistance coefficient field and risk field by using a potential field method and a diffusion equation respectively, finally, the environmental resistance factor in the process operation space is calculated to correct the process prediction duration, and iterative control is performed based on a field-graph closed loop feedback mechanism. The application can quantize the spatial congestion effect and risk non-uniform conduction under a complex building environment, solves the problem of logical plan and physical constraint separation, and significantly improves the accuracy of construction progress prediction.

This invention provides a method for generating a seabed DEM based on large-scale nautical chart depth points and high-resolution remote sensing surfaces, belonging to the field of marine surveying technology. The method includes: acquiring discrete depth points from remote sensing images and nautical charts; extracting a relative depth trend surface based on a dual-band logarithmic ratio model; calculating the image gradient field and pixel-level structure tensor, solving for the principal eigenvectors, and constructing an anisotropic diffusion tensor by combining local gradient magnitudes; using the nautical chart depth points to perform regression calibration on the trend surface to generate sparse residuals, and establishing a residual calculation grid; using the anisotropic diffusion tensor as a guiding constraint, performing non-uniform diffusion calculation on the sparse residuals to generate a global continuous residual correction surface; superimposing the correction surface onto the trend surface, and obtaining the final seabed DEM through anomaly removal and edge smoothing. This invention solves the problems of low accuracy and loss of detail in complex shallow sea topography inversion.

The invention relates to the technical field of medical image processing, in particular to an ultrasonic image processing system based on image enhancement, which comprises a field construction step: constructing a pixel-level structure tensor matrix, and mapping original ultrasonic image data into tensor field data; a flow direction locking step: performing feature space decomposition on the tensor field data to generate a diffusion guide graph containing a main feature vector; a nonlinear evolution step: constructing an anisotropic diffusion tensor based on characteristic value information, and executing time step iterative solution based on a partial differential equation; a reunion output step: performing boundary continuity reunion on the enhanced image data; according to the method, the direction of the tissue is accurately locked through the Riemannian metric tensor field, smooth texture smoothing and inverse texture sharpening are achieved, speckle noise is greatly restrained, and meanwhile the microstructure features are reserved.

This invention relates to the field of computer-aided engineering technology, specifically a method for dynamically predicting the stain-resistant area of ​​dark-colored pure cotton fabric based on a dye diffusion model. The method acquires a parameter dataset characterizing the fabric's microstructure, dye properties, and printing process. Based on this, a multi-scale coupled mathematical model is constructed to predict the dye diffusion range. The model decomposes the diffusion process into macroscopic inter-yarn diffusion and microscopic intra-fiber diffusion, using anisotropic diffusion tensors to characterize the difference in dye diffusion rates along the fabric direction. Through structure-aware finite element analysis, the model calculates the spatiotemporal concentration distribution of the dye and generates geometric data for predicting the stain-resistant boundary based on a preset concentration threshold. Based on this data, the printing process parameters and model parameters are adaptively adjusted to achieve dynamic prediction of the stain-resistant area. This method utilizes computer simulation and finite element analysis techniques to improve the accuracy of dynamic prediction of the stain-resistant area on dark-colored pure cotton fabric.

An illumination device includes an asymmetric laser light source, a beam adjustment module and an anisotropic diffusion module. The asymmetric laser light source is adapted to output an illumination light beam having different sizes in a first direction and a second direction that are interlaced with each other. The beam adjustment module is adapted to adjust a beam size of the illumination light beam. The anisotropic diffusion module is disposed between the asymmetric laser light source and the beam adjustment module. The anisotropic diffusion module has different optical properties along the first direction and the second direction, and the beam size of the illumination light beam is consistent in the first direction and the second direction.