30 results about "Nonlinear diffusion" patented technology

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 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 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.

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 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.

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 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.