30 results about "Nonlinear diffusion" patented technology

The invention discloses a fingerprint image enhancing method, and belongs to the technical field of image processing. The method comprises the following steps of: performing gray shearing and gray stretching on fingerprint foreground region image gray to obtain fingerprint foreground region image gray2; performing Gaussian filtration on the fingerprint foreground region image gray2 by using a Gaussian filter g sigma, and calculating a normalized fingerprint gradient vector; then constructing a structure tensor, and calculating the direction field of fingerprint ridge lines; and finally, constructing two one-dimensional anisotropic filters to perform two-time filtration enhancement on the fingerprint foreground region image gray2 to obtain a final filtration enhanced fingerprint foreground region image. Based on a nonlinear diffusion model and the normalized structure tensor, the method can effectively remove various structural noises, reduce the dynamic change range between the fingerprint ridge lines and the valley lines and connect certain broken fingerprint ridge lines at the same time. The method has good enhancement effect and characteristic of effectively saving the time, and can meet the requirement of on-line fingerprint identification.

The invention is a method of developing a sedimentary basin from a stratigraphic simulation of multilithologic filling accounting for fine sediment transport. The sedimentary basin is broken up into geologic layers with each geologic layer being subdivided into a series of climatic layers. Each climatic layer is associated with at least one constant climatic parameter. A stratigraphic simulation is performed within each climatic layer using a stratigraphic model. The sediment transport in the continental domain and sediment bottom layer transport in the marine and lacustrine domains are modelled using nonlinear diffusion equations under maximum erosion rate constraint. Suspended sediment transport in the marine and lacustrine domain is modeled using an advection-diffusion equation accounting for a particle fall rate and of a marine and lacustrine current velocity. The climatic layers belonging to a single geologic layer are then homogenized and the basin is developed according to the stratigraphic simulation results.

A method for detecting and identifying structures of interest such as colonic polyps or similar structures like lung nodules in volumetric (medical) images data is provided. The method includes obtaining a heat diffusion field (HDF) by applying a heat diffusion scheme to a volume of interest that includes structures. The obtained heat diffusion field is then used for identifying a structure of interest from the structures in the volume of interest using a geometrical analysis of the heat diffusion field. The heat diffusion scheme is, at least partly, governed by non-linear diffusion parameters. The identification includes two parts: (i) the computation of a spherical symmetry parameter, and (ii) the performance of a local analysis of the volume of interest and computation of a triangulization parameter.

The invention discloses an equipment residual life prediction method and system. The method comprises the following steps: establishing an equipment degradation mathematical model based on a nonlineardiffusion process; obtaining an estimated value of the equipment degradation parameter under the acceleration stress as first data; calculating a degradation parameter value under normal working stress according to the first data to obtain second data; determining the distribution type of the second data through goodness-of-fit test; obtaining a posteriori distribution function and an expected value of the second data according to the distribution type of the second data; obtaining a first residual life probability density function according to the posteriori distribution function, the expected value and the equipment degradation mathematical model; correcting the first residual life probability density function according to a total probability formula to obtain a second residual life probability density function; and predicting the residual life of the equipment according to the second residual life probability density function. The prediction precision of the residual life of the equipment can be improved.

The invention discloses an application for detecting and tracking an infrared weak object under a complicated background. The application is characterized by comprising the following steps of: 1. suppressing clutters and keeping the topological structure of an image, and constructing a bionic vision weighted entropy model with an adjacent airspace and a preferred direction to realize conversion for the image from a grey mode to an entropy model; 2. analysing the movement state of the weak object with burst and stationary characteristics, and constructing a self-adaptive entropy flow target movement estimation model meeting the maneuvering features of the weak object by virtue of the nonlinear diffusion smoothing and self-adaptive local restriction criterion of an entropy flow to realize the approximation of an estimation speed to the real movement state of the weak object; 3. searching a weak object tracking method adopting generic multi-feature and measurement, and constructing a multi-feature fused sequential filter model to realize accurate, robust and real-time identification for the weak object. The invention discloses a self-adaptive entropy flow detection and tracking algorithm for the infrared weak object, and enriches a detection and tracking technology for the weak object.

The invention relates to an atmospheric pollution diffusion simulation method and simulation system. Based on modeling simulation of Agents, a modeling simulation method of the Agents is applied to atmospheric pollution influence; through the Agents, an atmospheric main body in a complex environmental system is described; an interactive mode among the atmospheric Agents is modeled; and the atmospheric Agents are abstracted out and subjected to formalized model design, so that dynamic evaluation of the atmospheric pollution influence is realized. The diffusion of pollutants along with motion ofthe atmospheric Agents and the influence of atmospheric conditions such as rainfall, a wind direction, a wind velocity, atmospheric stability and the like in a simulation region on pollutant propagation can be effectively considered; and a complex and nonlinear diffusion process of atmospheric pollution is displayed.

The invention relates to a method for removing block noise from a video image, which comprises the following steps of: performing entropy decoding, reordering, inverse qualification, inverse transformation and filtration with a filter on the video image to obtain a reconstructed image, and performing nonlinear diffusion image filtering processing on the reconstructed image. The step of performing nonlinear diffusion image filtering processing comprises the following steps of: firstly, calculating the gradient absolute value of each pixel in the image and the isolux curvature absolute value of the image and determining the diffusion coefficient value of each pixel according to the two parameters; secondly, performing nonlinear diffusion algorithm to remove the block noise, namely determining the degree of diffusion according to the diffusion coefficient and updating the gray-scale value of the pixel at the same time; and finally calculating the signal to noise ratio of the image diffusion result, if the signal to noise ratio is higher than a given value, finishing the image processing, otherwise, returning to the last step, namely updating the gradient absolute value and the curvature absolute value until the signal to noise ratio is higher than the given value. The method of the invention is suitable for filtering the block noise from the reconstructed image, can protect edges and has very good effect.

The invention discloses an image-guided adaptive algorithm based on edge-preserving multi-scale deformable registration, which includes the following steps that: (1) on the basis of a nonlinear diffusion TV (total variation) model, an edge-preserving multi-scale space is constructed, and carries out multi-scale decomposition on an image according to the geometric dimension of the contour structure in the image; (2) the multi-scale space is combined with a coarse-to-fine free-form deformation grid model, and according to the complexity of multi-scale image details, deformation grid density is adaptively adjusted; (3) by automatically estimating the smoothing parameter in the nonlinear diffusion model Lambda, a full-automatic multi-scale registration scheme is constructed. The algorithm can effectively increase the precision, speed and robustness of deformable registration.

The invention discloses a low-dose computed tomography (CT) image processing method based on wavelet space directional filtering, belonging to the technical field of computerized tomography. The method is as follows: firstly, static wavelet transform is used for carrying out single-layer decomposition on the low-dose CT image to be processed, then high-frequency detailed images in the horizontal, vertical and opposite angle directions are subjected to one-dimensional nonlinear diffusion filtering in the vertical and horizontal directions respectively so as to restrain the information intensity of star-strip artifacts in high-frequency detailed images in different directions, and then inverse static wavelet transform is conducted according to the processed high-frequency detailed images in the horizontal, vertical and opposite angle directions and the original low-frequency images for rebuilding to obtain artifacts so as to obtain restrained CT images, finally, the image is further processed by using the existing large adjacent region weighted average noise suppression method. By utilizing the method, the star-strip artifacts and noise in the low-dose CT image can be effectively restrained, and the quality of the low-dose CT image can be improved, so that the low dose CT image meets the quality requirements of clinical diagnosis.

The invention relates to a rumor detection method based on linear and nonlinear propagation, and belongs to the technical field of natural language understanding. According to the method, unified modeling representation is carried out on rumor nodes by utilizing text content and time information, and rumor detection is automatically carried out in a mode of combining linear and nonlinear propagation characteristics. Firstly, text information and time information contained in rumor nodes are used for carrying out joint representation on mixed features of the rumor nodes; then, node informationis aggregated along the linear time sequence and the nonlinear diffusion structure, expression of a source node is enhanced, and final propagation representation is formed. And finally, authenticity label prediction is carried out by using propagation representation. According to the method, node characteristics of rumors are extracted from two different angles, tree perception representation is obtained from a nonlinear diffusion mode, characteristics of propagation sequences are captured from linear time sequence interaction, and authenticity of the rumors can be accurately predicted.

Aiming at the situation that long-life and high-reliability equipment lacks sufficient degradation data, the invention provides a degradation equipment residual life prediction method considering multiple uncertainties, and the method comprises a stepping accelerated degradation model based on a nonlinear diffusion process, and the model has the advantages that only a small sample size and short test time are needed. For inherent characteristics, individual differences and multiple uncertainties caused by human deviation in the process of measuring equipment performance and degradation of a degradation model, the model considers time-varying uncertainties, individual differences and performance degradation and covariable measurement uncertainties. In order to estimate the residual life of the degraded equipment, an analytic approximate solution that the nonlinear diffusion process passes through a preset threshold value under the sense of first arrival time is obtained through derivation. A maximum likelihood estimation (MLE) method and a simulation extrapolation (SIMEX) method are combined to obtain an MME-SIMEX method which is used for estimating unknown parameters in a model. The effectiveness of the model provided by the invention is proved through simulation and actual cases. The result shows that the method has higher residual life estimation precision and has certain engineering practical value.

Aspects of the present invention are related to low complexity systems and methods for nonlinear diffusion filtering of a motion-vector field. Local weights in the nonlinear diffusion filter may be data-adaptive, and, according to one aspect of the present invention, may be determined such that spatial coherency may not be enforced at object boundaries although the object boundaries have not been explicitly detected. Thus, the methods and systems of the present invention may smooth a motion-vector field without smoothing the motion-vector field across an object boundary. According to a second aspect of the present invention, motion vectors with a low confidence value may be suppressed, while motion vectors with a high confidence value may be propagated. According to another aspect of the present invention, motion estimation methods and systems may incorporate the methods and systems of the nonlinear diffusion filtering according to the present invention.

The invention discloses an image restoration method based on curvature-driven diffusion. A nonnegative, smooth and monotonically decreasing guiding function g(s) is introduced through a nonlinear diffusion term portion of a curvature driven diffusion (CDD) model, a self-adaptive CDD model is designed, wherein g(0) is equal to 1, a parameter s is a gradient module value, m is a regulatory factor of a margin guiding function decreasing speed, k is a threshold parameter, and median is mean value operation. The guiding function g(s) changes with change of the parameter s, the diffusion factor changes, and therefore restoration intensity self-adaptive control of an image smooth area and a texture and margin area is achieved. The self-adaptive model is better than a total variation (TV) model and the CDD model when a large scale damaged area is restored, image margin and detail information can be kept well, and vision effect is good.

The invention discloses a selective image stylizing method based on nonlinear filtering, which is characterized by comprising the following steps of: (1) simple user interaction; (2) nonlinear filtering; (3) line extraction; and finally output of images which harmoniously combining senses of reality and unreality. In the method, nonlinear diffusion filtering is improved by the following steps of: 1. combining nonlinear diffusion filtering with bilateral filtering; 2. adding a stylizing level control item in an equation; and 3. combining isotropic filtering with anisotropic filtering, so that the nonlinear diffusion filtering can be more suitable for the application of selectivity stylizing. The invention also has the advantages of providing a new artistic style which is an artistic effect of harmonious combination of real objects and stylized scenes, ensuring that a greater contrast is formed between a foreground and a background for giving prominence to the theme of pictures, and being capable of simulating the effect of changing the depth range of fields of a camera.

The invention discloses an application of detection and tracking of weak infrared targets under complex backgrounds. The characteristic steps are: 1. Suppress clutter and maintain the topological structure of the image, construct a bionic visual weighted entropy model with spatial proximity and direction priority, and realize image Transform from grayscale mode to entropy mode; 2. Analyze the sudden or steady motion state of weak targets, and use the non-linear diffusion smoothing of entropy flow and adaptive local constraint criteria to construct adaptive entropy flow targets that conform to the maneuvering characteristics of weak targets The motion estimation model realizes the estimated speed approaching the real motion state of the weak target; 3. Researches the weak target tracking method of multi-feature fusion and measurement, constructs a sequential filtering model of multi-feature fusion, and realizes the accuracy, robustness and accuracy of weak targets real-time recognition. The invention proposes an algorithm of adaptive entropy flow detection and tracking of infrared weak targets, which enriches the technology of weak target detection and tracking.

The invention discloses a fingerprint image enhancing method, and belongs to the technical field of image processing. The method comprises the following steps of: performing gray shearing and gray stretching on fingerprint foreground region image gray to obtain fingerprint foreground region image gray2; performing Gaussian filtration on the fingerprint foreground region image gray2 by using a Gaussian filter g sigma, and calculating a normalized fingerprint gradient vector; then constructing a structure tensor, and calculating the direction field of fingerprint ridge lines; and finally, constructing two one-dimensional anisotropic filters to perform two-time filtration enhancement on the fingerprint foreground region image gray2 to obtain a final filtration enhanced fingerprint foregroundregion image. Based on a nonlinear diffusion model and the normalized structure tensor, the method can effectively remove various structural noises, reduce the dynamic change range between the fingerprint ridge lines and the valley lines and connect certain broken fingerprint ridge lines at the same time. The method has good enhancement effect and characteristic of effectively saving the time, and can meet the requirement of on-line fingerprint identification.

The invention provides an ultrasonic image processing method and device, and aims to solve the problem of how to improve the definition and contrast of edges of ultrasonic images on an image level. According to the invention, ultrasonic images are subjected to multi-scale decomposition, images of at least one scale is subjected to edge extraction, feature values and feature vectors of the images of the scale are obtained based on a structure tensor matrix, then the diffusion tensor is constructed based on the feature values and the feature vectors to improve the image quality, and therefore, the ultrasonic images are optimized by adopting a nonlinear diffusion method combined with an edge detection algorithm, and the edges and a contrast ratio of the ultrasonic images of the scale are optimized; then the ultrasonic images with multiple scales are reconstructed to obtain a high-quality ultrasonic image.

The invention discloses an equipment residual life prediction method and system. The method comprises the following steps: establishing an equipment degradation mathematical model based on a nonlineardiffusion process; obtaining an estimated value of the equipment degradation parameter under the acceleration stress as first data; calculating a degradation parameter value under normal working stress according to the first data to obtain second data; determining the distribution type of the second data through goodness-of-fit test; obtaining a posteriori distribution function and an expected value of the second data according to the distribution type of the second data; obtaining a first residual life probability density function according to the posteriori distribution function, the expected value and the equipment degradation mathematical model; correcting the first residual life probability density function according to a total probability formula to obtain a second residual life probability density function; and predicting the residual life of the equipment according to the second residual life probability density function. The prediction precision of the residual life of the equipment can be improved.

The invention discloses a low-dose computed tomography (CT) image processing method based on wavelet space directional filtering, belonging to the technical field of computerized tomography. The method is as follows: firstly, static wavelet transform is used for carrying out single-layer decomposition on the low-dose CT image to be processed, then high-frequency detailed images in the horizontal,vertical and opposite angle directions are subjected to one-dimensional nonlinear diffusion filtering in the vertical and horizontal directions respectively so as to restrain the information intensity of star-strip artifacts in high-frequency detailed images in different directions, and then inverse static wavelet transform is conducted according to the processed high-frequency detailed images inthe horizontal, vertical and opposite angle directions and the original low-frequency images for rebuilding to obtain artifacts so as to obtain restrained CT images, finally, the image is further processed by using the existing large adjacent region weighted average noise suppression method. By utilizing the method, the star-strip artifacts and noise in the low-dose CT image can be effectively restrained, and the quality of the low-dose CT image can be improved, so that the low dose CT image meets the quality requirements of clinical diagnosis.

Information to be stored is input to a plurality of information carrier storage means that interact with each other. A unit amount of information carriers is added to a position selected from the information carrier storage means, and nonlinear diffusion of information carriers is executed from the selected information carrier storage means to its neighboring information carrier storage means, and at the same time, nonlinear dissipation is executed from the information carrier storage means. At that time, the dissipation amount is determined so that the amount adding the difference between the amount of addition and the amount of dissipation to the input representation does not exceed a predetermined threshold value. When equilibrium is confirmed as a result of judgement whether changes in amount of information carriers of each information carrier storage means has become small or not, the amount of information carriers stored in the information carrier storage means is output as a numerical value.

Provided are a method for extracting differential signals and a differentiator. The method comprises a first step of calculating a first integral signal z0 according to an output signal u of the differentiator, and calculating an error signal according to the first integral signal z0 and an input signal; a second step of calculating a second integral signal according to the error signal; a third step of constructing a nonlinear diffusion model according to the second integral signal and the error signal, and calculating a differential signal of the input signal according to the nonlinear diffusion model. According to the method, extraction of the input signal approximate differential is achieved by adopting the nonlinear diffusion model according to a computer construction error and an integral thereof. Compared with an existing differential signal extraction method, according to the method, not only is the implementation cost lower, but also the precision of the differential signals obtained by extraction is also higher.

The embodiment of the invention provides a spectrum denoising method and a processor. The method comprises the following steps: acquiring a to-be-denoised spectrum; determining gradient information of the to-be-denoised spectrum; determining a time modulation function through the gradient information; adjusting a preset nonlinear spread function according to the time modulation function; and performing iterative denoising on the to-be-denoised spectrum according to the adjusted nonlinear spread function. The time modulation function is determined to adjust the non-linear spread function, and then the to-be-denoised spectrum is subjected to iterative denoising according to the adjusted non-linear spread function, so that effective adjustment of a spatial non-linear filtering operator for the diffusion time is realized, and local peak information of the spectrum in the spectrum information denoising process is reserved with high precision.

The present invention relates to blood flow reserve fraction, and in particular to a method for obtaining vascular blood flow reserve fraction. The non-linear diffusion filter of the differential equation denoises the sequence image data; C. Segments the blood vessel image from the background image; D. Performs wavelet edge detection on the blood vessel image, and extracts the edge of the blood vessel image by decomposing the mutation point of the image E. Obtain the vascular blood flow reserve fraction according to the time interval between the vascular image and each frame of image; A method for obtaining fractional blood flow reserve, and a corresponding device.

The invention discloses a selective image stylizing method based on nonlinear filtering, which is characterized by comprising the following steps of: (1) simple user interaction; (2) nonlinear filtering; (3) line extraction; and finally output of images which harmoniously combining senses of reality and unreality. In the method, nonlinear diffusion filtering is improved by the following steps of: 1. combining nonlinear diffusion filtering with bilateral filtering; 2. adding a stylizing level control item in an equation; and 3. combining isotropic filtering with anisotropic filtering, so that the nonlinear diffusion filtering can be more suitable for the application of selectivity stylizing. The invention also has the advantages of providing a new artistic style which is an artistic effect of harmonious combination of real objects and stylized scenes, ensuring that a greater contrast is formed between a foreground and a background for giving prominence to the theme of pictures, and being capable of simulating the effect of changing the depth range of fields of a camera.

The invention discloses an image compression encryption method based on compressed sensing and nonlinear diffusion, and belongs to the technical field of information security. The invention provides the image compression encryption method based on compressed sensing and non-linear diffusion, and aims to solve the problem of security risk due to reservation of statistical information of an image during image encryption of compressed sensing. Firstly, sparse representation is carried out on an original image, then a compressed sensing strategy is carried out, then a measurement value obtained through compressed sensing is encrypted through shifting, exclusive OR and other operations, and a final ciphertext is obtained. Through experimental verification and analysis, the image compression andencryption performance of the method is excellent, the image compression is carried out while the image encryption security is ensured, the calculation overhead and the storage overhead are reduced as much as possible, and the method can be widely applied to a resource-limited environment.

Embodiments of the present invention provide a spectral denoising method and a processor. The method includes: obtaining the spectrum to be denoised; determining the gradient information of the spectrum to be denoised; determining the time modulation function through the gradient information; adjusting the preset nonlinear diffusion function according to the time modulation function; Denoising spectra for iterative denoising. By determining the time modulation function to adjust the nonlinear diffusion function, and then iteratively denoising the spectrum to be denoised according to the adjusted nonlinear diffusion function, the effective adjustment of the spatial nonlinear filter operator for the diffusion time is realized, and high-precision The local peak information of the spectrum in the process of denoising the spectral information is preserved.

The invention relates to an anisotropic diffusion operation-based multi-scale detection image feature matching method, which comprises the following specific steps of: introducing anisotropic diffusion to filter an image to be processed, and then solving a nonlinear diffusion filter equation by using a mathematical framework rapid explicit diffusion scheme to obtain an anisotropic diffusion image; a nonlinear scale space is established; after the scale space is successfully established, entering a feature detection stage, calculating a response value of each point in the image through a Hessian matrix, and detecting a local maximum point in the image according to the response value as a feature point; the feature points are endowed with main directions, descriptor generation operation is carried out by using a specific algorithm, and then image feature matching is carried out by using descriptor similarity measurement; the method is applied to a natural feature-based tracking registration technology, and has a strong practical value in an environment with relatively high requirements on real-time performance and effect.

The invention relates to an atmospheric pollution diffusion simulation method and simulation system. Based on modeling simulation of Agents, a modeling simulation method of the Agents is applied to atmospheric pollution influence; through the Agents, an atmospheric main body in a complex environmental system is described; an interactive mode among the atmospheric Agents is modeled; and the atmospheric Agents are abstracted out and subjected to formalized model design, so that dynamic evaluation of the atmospheric pollution influence is realized. The diffusion of pollutants along with motion ofthe atmospheric Agents and the influence of atmospheric conditions such as rainfall, a wind direction, a wind velocity, atmospheric stability and the like in a simulation region on pollutant propagation can be effectively considered; and a complex and nonlinear diffusion process of atmospheric pollution is displayed.