76 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 discloses a multi-modal thermal imager intelligent monitoring method based on image processing. The method comprises the following steps: S1, performing dual-band synchronous imaging; s2, carrying out cross-modal image registration; s3, multispectral image fusion: performing smoothing processing on the infrared and visible light images through anisotropic diffusion to obtain a main structure layer image, calculating to obtain an infrared and visible light detail layer image, and linearly combining the detail layer image through PCA principal component weight, and finally, combining the detail layer image and the main structure layer image according to the self-defined weight to obtain a final fusion image. And S4, gas leakage detection is carried out, so that gas leakage detection and early warning can be realized. According to the invention, a gas enhancement algorithm is adopted to realize real-time detection of leaked gas, interference of most background noise can be eliminated, and the real-time performance and reliability of the system are improved.

The invention relates to the technical field of multi-modal sensing, and discloses a multi-modal large model detection and recognition robot recognition system for a complex scene, the system constructs a dynamic manifold modeling module, realizes cross-modal joint denoising through a stochastic differential equation and depth score matching, constructs a drift term and an anisotropic diffusion term by using an optical flow field, and realizes multi-modal detection and recognition of a multi-modal large model. Dynamic noise interference such as rain fog and motion blur is eliminated; on the basis, designing an information geometric alignment module, and based on Riemannian manifold optimization and orthogonal projection matrix calculation, realizing geometric equidistant mapping of vision-Li DAR features through multi-scale measurement tensor fusion; a dynamic external parameter calibration module is further provided, SE (3) manifold Kalman filtering is combined with a noise self-adaptive scaling technology, and external parameter offset is tracked and compensated in real time. Compared with a traditional method, the method has the advantages that the core problems of cross-modal data geometric mismatch, external parameter drift accumulation, low semantic fusion efficiency and the like are solved, and the sensing precision and robustness of the automatic driving system in a complex dynamic scene are remarkably improved.

The invention discloses a thermal management control method and device for a new energy automobile battery, and relates to the field of battery thermal management control, the device comprises an entropy change module, a fault module, a decision module, a phase change module and a scheduling module, entropy change intensity and nuclear heat flow field analysis is carried out on collected current and voltage signals to obtain an aging thermal topology, and the aging thermal topology is subjected to thermal management control. The method comprises the following steps: constructing an anisotropic diffusion equation based on aging thermal topology in combination with a fault diffusion tensor, discretizing a sub-strategy set for a safety margin field, constructing a cooling Nash game, analyzing through a payment matrix and a replicated dynamic equation, screening a stable strategy, mapping, and constructing an implicit phase change criterion model for the surface temperature of a battery to obtain a phase change cumulant; the phase change cumulant latent heat is analyzed and then cooperatively arbitrated with the cooling membership degree to obtain arbitration cooling capacity, the water pump flow is obtained through limiting operation based on the arbitration cooling capacity and the temperature deviation, then cubic root mapping is conducted on the water pump flow and the arbitration cooling capacity to obtain the fan rotating speed, and accurate control over battery thermal management is achieved.

The invention belongs to the technical field of image processing, particularly relates to an image effect enhancement method and system, and aims to improve the image quality through multi-domain collaborative adaptive processing. The method comprises the following steps: firstly, determining an optimal quantization level through content-aware adaptive quantization, generating a quantization image and a region complexity graph, then decomposing the image into smooth, edge and texture sub-bands through multi-scale domain analysis, constructing a characteristic spectrum, then, applying histogram equalization, anisotropic diffusion and nonlinear sharpening to different sub-bands through adaptive characteristic enhancement, and finally, carrying out adaptive characteristic enhancement on the different sub-bands. And residual error optimization is carried out. And finally, multi-domain collaborative fusion is carried out to construct an enhancement matrix, fusion masks and feature correction are applied to obtain a final enhanced image, and the method is suitable for various images, retains details, enhances different regions in a targeted manner, and has a remarkable effect.

The invention relates to the technical field of data processing, in particular to a high-noise data intelligent cleaning and credibility evaluation method based on uncertainty quantization, which comprises the following steps: constructing a noise manifold model of a high-dimensional data space through a Riemannian geometry method, and extracting data intrinsic structure features and noise distribution trajectory tensor; according to the noise distribution trajectory tensor, constructing an anisotropic diffusion equation, and generating confidence distribution with data manifold constraint; the geometric invariance of a data set is kept while noise stripping is carried out in a data manifold tangent space, and noise stripping and structure keeping are achieved; calculating an intrinsic credibility index and generating a credibility evaluation map coupled with the data manifold structure; according to the method, the intrinsic characteristics of the data manifold are kept in the cleaning process, the deformation degree of the quantized data structure is calculated through the geodesic deviation, and it is ensured that the intrinsic geometrical relationship of the data is not affected by noise stripping.

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 relates to the technical field of computer-aided engineering, in particular to a pure cotton dark-color fabric dye-resistant area dynamic prediction method based on a dye diffusion model. The method comprises the following steps: acquiring a parameter data set representing a fabric microstructure, dye properties and a printing process; on the basis, a multi-scale coupling mathematical model is constructed, the dye diffusion range is predicted, the diffusion process is decomposed into seepage diffusion between macroscopic yarns and diffusion in microcosmic fibers through the model, and the diffusion rate difference of the dye in the fabric direction is represented through anisotropic diffusion tensors; through finite element analysis of structure perception, running a model to calculate dye space-time concentration distribution, and generating geometric data for predicting a bleeding boundary according to a preset concentration threshold value; according to the data, printing process parameters and model parameters are adjusted in a self-adaptive mode, and dynamic prediction of the dye-resistant area is achieved; according to the method, computer simulation and finite element analysis technologies are utilized, and the accuracy of dynamic prediction of the dyeing-resistant area of the pure cotton dark-color fabric is 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 the field of image processing, and particularly discloses a food production line real-time monitoring method and system based on image processing, and the method comprises the following steps: S1, obtaining an original image of to-be-monitored food, carrying out the graying of the original image, and carrying out the smoothing of the original image through employing an anisotropic diffusion model with the diffusion coefficient being inversely proportional to the gradient amplitude, and obtaining a preprocessed image; s2, calculating a gradient magnitude and a gradient direction of each pixel point in the preprocessed image; and S3, for any pixel point in the preprocessed image, calculating the fractal dimension and local phase consistency of a window area of the pixel point, obtaining an adjustment coefficient from a preset lookup table based on the two feature values, and adjusting a preset reference threshold by using the adjustment coefficient to obtain a dynamic high threshold Thigh. The method has the beneficial effects that the judgment basis of edge connection is more sufficient, more complete, continuous and smooth food contours can be generated, and fracture and burrs of the contours are remarkably reduced.

The invention relates to the technical field of geophysical exploration, in particular to a method for performing well leakage early warning by utilizing an earthquake prediction result, which comprises the steps of basic data acquisition, regional geological condition analysis, earthquake data preprocessing, small fracture and fracture-cavity earthquake fine description and shadow relief display of a description result. According to the method, the quality of basic data is improved by introducing anisotropic diffusion and a three-dimensional multi-stage median joint filtering technology, multi-attribute fusion based on adjustable parameters is carried out on pre-stack multi-azimuth frequency attenuation gradient crack prediction and post-stack structural coherent tensor, and finally, a multi-attribute fusion result is spatially displayed by utilizing a shadow relief technology. The precision of small fracture and fracture-cavity body depicting is improved, the spatial forms of fractures and fracture-cavity bodies are displayed more visually, and well position design, well drilling design and well drilling trajectory optimization can be effectively guided.

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 present invention relates to an image enhancement method for constructing a spatial visual neural network model. The present invention first normalizes a binary noisy two-dimensional image to satisfy the small parameter restriction imposed by the adiabatic approximation condition for activating FHN neurons; secondly, convolves the spatial signal with an FHN neuron template to obtain a two-dimensional image encoding signal of a spatial neuron pulse potential sequence; constructs an anisotropic diffusion function based on improved bidirectional adaptive selection as a visual receptive field model with a directional selection mechanism, convolves the neuron pulse encoding signal with the constructed receptive field model; and finally finds the optimal binary image result. The invention effectively combines the directional selectivity of the biological visual system and the neuron pulse emission mechanism, making it conform to the physiological characteristics of real visual neuron pulse triggering and signal selectivity, thereby achieving a relatively ideal image denoising effect.

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.

The invention discloses a seismic data denoising method based on an improved anisotropic diffusion model. The method comprises the following steps: 1) obtaining original noise data to be denoised; 2) data preprocessing: performing Gaussian filtering processing on the original seismic data to obtain preprocessed seismic data; 3) constructing an improved anisotropic diffusion model; 4) setting an iteration error limit and parameters in the model; and 5) performing iterative calculation on the noise-containing data according to the improved anisotropic diffusion model to obtain an iterative result which is a seismic data denoising result. The seismic data denoising method is obtained by improving the anisotropic diffusion model, and the edge preserving and denoising effects of the seismic data are improved.

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.