26 results about "Multiplicative noise" patented technology

The invention discloses a synthetic aperture radar image speckle suppression method based on diffusion sampling, and relates to the field of synthetic aperture radar image processing. The method is based on a sampling scheme of an approximate score function, inverse transfer distribution and application of a multiplicative noise model, solves the problem of multiplicative noise degradation in the synthetic aperture radar image, and avoids supervised training and logarithmic transformation of the SAR image.

The invention relates to the technical field of information fusion, in particular to a robust filtering method for solving uncertainty of one-step random observation time delay, which comprises the following steps of: taking multiplicative noise, one-step random time delay, data packet loss, continuous loss observation and uncertain noise variance which are dependent on a system state and a state in an observation matrix into consideration; specifically, based on a maximum and minimum robust estimation principle, a CI fusion Kalman estimator (a predictor, a filter and a smoother) and two FCI fusion Kalman estimators are provided. According to the method, an augmentation method and a virtual noise method are combined, a robustness proving method based on permutation matrixes and a Lyapunov equation is introduced, it is proved that the proposed fusion estimator has robustness by defining different permutation matrixes, and in addition, through an application example of autoregressive moving average signal processing, the robustness of the fusion estimator is improved. It is verified that the robust local and fusion signal estimation problem can be solved through the state estimation method, and therefore solid practical application support is provided for the system modeling result.

The present invention discloses a strain adaptive calculation method for optical coherence elastic imaging, which reconstructs the strain field distribution inside a composite material with high precision through an adaptive rotation model of a differential phase combined with a light vector method. The present invention adopts an algorithm combining an adaptive rotation model with a vector method, and does not have the problem of being unable to improve the strain calculation signal-to-noise ratio under multiplicative noise when using a least squares method. In addition, the present invention finds the optimal rotation angle by constructing an adaptive rotation model of a differential phase, and then combines the light vector method to reduce its own lateral inhomogeneity to a certain extent, thereby solving the problem that the light vector method cannot accurately construct the strain field distribution inside a composite material with a low signal-to-noise ratio under strong noise and complex deformation conditions, especially when the lateral changes are abnormally non-uniform.

The invention provides a three-dimensional seismic data random noise suppression method based on a spatial regularization feature fusion network. The seismic data denoising method comprises the following steps: firstly, dividing a data set into training data and test data in proportion, and adding random noise containing Gaussian noise and multiplicative noise at different levels into the training data; random noise containing different levels of Gaussian noise, multiplicative noise and salt and pepper noise is added to the training data; in the training process, the data are sent into a multi-level feature extraction module to adaptively acquire different feature information of the seismic data; a parallel attention gating fusion module is used for separating signals and noise, more effective information is reserved, and more noise is removed; structural features are corrected through a spatial structure correction module, and the data reconstruction capability of the model is enhanced. The network provided by the invention has high generalization and accuracy, and especially has good performance in the aspect of denoising three-dimensional seismic data containing various types of random noise.

The invention discloses a diffraction image quality improving method based on intrinsic stray light correction, and belongs to the technical field of diffraction image processing. The method comprises the following steps: firstly, establishing a calibration sample library by shooting a multi-scene image so as to determine a frequency spectrum parameter and a weight parameter of intrinsic stray light of a camera; secondly, logarithmic transformation is carried out on the diffraction image, multiplicative noise is converted into additive noise, after Fourier transformation is carried out on the additive noise to a frequency domain, stray light stripping is carried out through a Gaussian homomorphic filter, and then inverse transformation is carried out to recover the image; and finally, carrying out low-high frequency information superposition on the corrected image based on a non-mask sharpening algorithm so as to further improve the definition. According to the method, intrinsic stray light in the diffraction image is effectively eliminated, the image contrast and detail performance are remarkably improved, and an effective back-end processing scheme is provided for practical application of the diffraction imaging technology.

The invention relates to a magnetic resonance signal correction method and device and a magnetic resonance imaging system. The method comprises the following steps: acquiring an original free induction attenuation signal of target equipment; determining a magnetic resonance signal correction curve of the target equipment according to the original free induction decay signal; and according to the magnetic resonance signal correction curve, correcting a to-be-corrected magnetic resonance signal collected by the target device to obtain a corrected magnetic resonance signal corresponding to the to-be-corrected magnetic resonance signal. By adopting the method, multiplicative noise interference of magnetic resonance signals can be removed, and the quality of magnetic resonance imaging is improved.

The invention provides a low-dose CBCT (cone beam computed tomography) image quality enhancement method, which more comprehensively considers the noise type in an image, can effectively suppress noise and can keep image details. According to the method, a three-dimensional low-dose CBCT image is firstly sliced to generate a single 2D image, and then noise estimation is carried out on the single 2D image by using a composite noise model; in the noise reduction stage, after removal of a spatial domain is achieved for additive noise, multiplicative noise is converted into additive noise and then separated into a high-frequency component and a low-frequency component, and a filtering method is designed in a targeted mode for precise noise reduction; after the original data structure of the image after noise reduction is recovered, edge recovery is carried out on edge pixels lost in noise reduction, the data after edge recovery is reconstructed into a three-dimensional image, and consistency adjustment is carried out on the three-dimensional image data based on the brightness between layers, so that the quality enhancement of the image is realized.

The invention relates to the technical field of signal processing, in particular to a filtering method for dealing with a system with complex multiplicative noise and random time delay uncertainty, which designs a reasonable system model of a multi-sensor networked system meeting conditions, and adopts a maximum and minimum robust estimation principle, a de-randomization method and a virtual noise technology. The system is converted into a multi-model multi-sensor system only with uncertain noise variance, an augmented noise method, a non-negative definite matrix factorization method and a Lyapunov equation method are applied, two robust centralized fusion steady-state Kalman estimators are obtained, and the robustness of the estimators is further proved. The method can be used for solving the problem of robust fusion Kalman filtering of multi-sensor single-channel ARMA signals with random parameter matrixes, uncertain noise variance and networked random uncertainty.

The present application belongs to the technical field of remote sensing image processing, and provides a SAR image denoising method and device based on a logarithmic domain diffusion model. The denoising method comprises: preprocessing: converting an original SAR amplitude image to a logarithmic domain to obtain a starting input image for a diffusion process; forward diffusion: constructing a forward diffusion path based on a non-central Gaussian distribution, simulating a noise injection process, and generating a noisy image sequence; neural network model noise prediction: performing feature extraction and fusion on the noisy image and physical metadata through a pre-constructed neural network model, and outputting a noise residual prediction result; backward sampling reconstruction: iteratively sampling using the noise residual prediction result to reconstruct a denoised SAR image. The denoising method solves the distribution mismatch problem of traditional diffusion models for SAR multiplicative noise, improves the denoising precision, and preserves the image texture and structure information.

The invention discloses a sonar video sequence denoising method and system, a readable medium and an underwater vehicle, and has the advantages that weak supervised learning under a non-registered dynamic video is realized, the limitation that input and output pixel level alignment is required by a traditional method is overcome, a supervised target is allowed to have non-rigid infinitesimal displacement in a local field, and the accuracy of the noise reduction of the sonar video sequence is improved. Sonar continuous video frames containing tiny non-rigid jitter are directly used for training. And constructing a time sequence-space combined lightweight mapping mechanism: redefining a channel mixing concept in the LUT, migrating from a space domain dimension to a time sequence dimension, and simultaneously realizing denoising and inter-frame smoothing through table look-up operation on the premise of not increasing the calculation amount. In order to solve the problem of LUT quantization loss under weak supervision, an anti-noise LUT conversion method is provided, it is ensured that under the condition of low bit wide storage, the description capability of a weak sonar target can still be kept, and logarithm transformation of an image is introduced so as to adapt to multiplicative noise caused by speckle noise.

The invention relates to the technical field of signal processing, in particular to a weighted state fusion filtering method for coping with noise and one-step random time delay and packet loss, which effectively captures the influence of colored multiplicative noise on the system state by introducing a virtual state vector and constructing a new augmented system model; a weighted state fusion filter is further deduced under a projective theory framework to adapt to the uncertainty of random time delay and data packet loss, and the filtering precision is improved. In addition, through the distributed structure design, effective state fusion can be carried out in a multi-sensor node system. And finally, through a plurality of simulation experiments, the excellent performance of the method in processing complex noise, time lag and packet loss is verified, and the advantages of the filter in precision and robustness are shown.

The application discloses a blood flow parameter determination and display method and device, an electronic device and a computer readable storage medium. The blood flow parameter determination method comprises the following steps: acquiring echo data of multiple pulses of wall-filtered pixel points in an imaging region; constructing four sub-sequences based on the echo data, and calculating fourth-order cumulants of the echo data based on the four sub-sequences; and calculating the blood flow parameter based on the echo data and the fourth-order cumulants. In the application, the four sub-sequences are constructed based on the echo data of multiple pulses of wall-filtered pixel points in the imaging region, the fourth-order cumulants of the echo data are calculated by using the constructed sub-sequences, and the problem that the autocorrelation method is affected by the accuracy of the blood flow parameter estimation in the additive Gaussian color noise and multiplicative noise background is eliminated by using the characteristics that the fourth-order cumulants are not sensitive to the additive Gaussian color noise and multiplicative noise, so that the accuracy of the blood flow parameter estimation is improved.

The present invention provides a method and system for extracting laser absorption spectrum by suppressing multiplicative noise. The system includes a test laser generation module, a non-absorbing laser generation module, an optical fiber coupler, an optical fiber splitter, an interferometer, a photodetector, a data acquisition module, an online compensation module and a computer. The test laser generated by the test laser generation module is split by a spectroscope. One beam is used by the interferometer to record the wavelength, and the other beam is combined with the sine-modulated laser generated by the non-absorbing laser generation module through the optical fiber coupler and then emitted by a collimating mirror. The combined beam passes through the gas to be measured and is detected by the photodetector. After being collected by the data acquisition module, the test laser and the non-absorbing laser are separated by the online compensation module, and the intensity change of the non-absorbing laser is used to compensate the intensity change of the test laser caused by the light deflection effect, so as to achieve accurate extraction of the absorption spectrum. The invention has a simple structure, can overcome the interference of the light deflection effect on the laser absorption spectrum, and has broad application prospects in the field of dynamic absorption spectrum testing such as combustion diagnosis.

The invention discloses a particle number state prediction method and device, equipment and a medium. The method comprises the steps of performing data prediction on current state information based on a target system model corresponding to a target particle counter and target noise parameter information to determine a basic state prediction value and a basic measurement prediction value corresponding to a target sample; performing covariance calculation on the basic state prediction value based on a preset singular value decomposition method to determine a prediction covariance error, and performing residual calculation on the basic measurement prediction value based on a preset residual rule to determine a measurement prediction error; and performing gain calculation on the prediction covariance error based on a preset adjustment fusion strategy to determine a target gain, and performing state updating on the basic state prediction value based on the target gain and the measurement prediction error to obtain a target state prediction value corresponding to the target sample. According to the technical scheme, the influence caused by multiplicative noise can be effectively reduced, and the accuracy of a particle number state result is improved.

The invention belongs to the technical field of image processing, and particularly discloses a fruit detection image denoising method based on graph signal processing, which comprises the following steps: acquiring an original fruit image, and constructing a multi-layer non-Euclidean graph structure model of the original fruit image; constructing a tight support biorthogonal graph wavelet filter bank, wherein the filter bank comprises an analysis filter bank and a comprehensive filter bank; performing multi-scale decomposition, filtering and inverse transformation reconstruction on the multi-layer non-Euclidean diagram structure model by using the tight support biorthogonal diagram wavelet filter bank to obtain a de-noised fruit image; the image preprocessing algorithm can effectively suppress noise caused by factors such as external and sudden unstable factors, uneven illumination, equipment vibration and the like of transmission channel characteristics when the industrial camera collects fruit images, and has a good denoising effect on multiplicative noise in the fruit images. And normal judgment of foreign matter detection of a visual system on a production line can be guaranteed.

The invention provides a double-comb absorption spectrum high-precision extraction method for gas parameter measurement, and belongs to the field of laser absorption spectrum measurement. A double-comb absorption spectrum is polluted by obvious multiplicative noise caused by optical power and coherent fluctuation of a laser source, and obvious standard deviation of the calculated gas temperature can be caused. The method provided by the invention can effectively realize separation of signals and noise, so that the influence of multiplicative noise on double-comb absorption spectrum gas parameter measurement is inhibited. According to the method, the correlation between noise and signals is suppressed by using operations such as logarithm operation, baseline offset, high-dimensional expansion and singular value decomposition, and effective separation of noise and signal subspaces is realized, so that noise-reduced absorption spectrum data is extracted. According to the invention, the dependence on a light source system and a self-adaptive acquisition system is reduced, and double-comb absorption spectrum measurement with high signal-to-noise ratio and high-precision gas parameter measurement can be realized.

The present invention discloses a method for estimating the parameters of an LFM signal under the influence of sinusoidal non-linear interference. First, the LFM signal is modeled, with the sinusoidal non-linear interference regarded as the "multiplicative noise" of the signal and the Gaussian noise regarded as the "additive noise" of the signal. The formed noise signal model includes a sinusoidal non-linear interference term, a Gaussian noise term, and an ideal signal model. Then, the noise signal model and the ideal signal model are used to train the CAE model. The trained CAE model can not only remove the conventional Gaussian noise in the signal but also remove the sinusoidal non-linear interference existing in the real environment. The method of the present invention then uses the "clean" signal from which the sinusoidal non-linear interference and Gaussian noise have been removed and the signal modulation parameters corresponding to the signal to train the CNN model. The trained CNN model can quickly complete parameter estimation and obtain accurate signal modulation parameters.

The invention provides a seismic image mixed noise suppression method based on a multi-attention feature adaptive fusion network. The image denoising method comprises the following steps: firstly, dividing a data set into training data and test data in proportion, and respectively adding mixed noise containing different multiples and Gaussian noise, multiplicative noise and salt and pepper noise at different levels into the training data and the test data; in the training process, data are sent into a dual-channel spatial attention module to obtain important channels and spatial position information of a seismic image, and important structure information of a noisy image is reconstructed; a separation information extraction module is used for separating feature information of different scales, and texture information of the image is analyzed; richer features are fused through the comprehensive feature adaptive fusion module, and more original fine feature information is reserved. The network provided by the invention has high generalization and accuracy, and especially has good performance in the aspect of seismic image denoising containing various random noises and multiple mixed noises.

A method and an apparatus for evaluating transmission impairments of a multiplexing converter. According to the method, impairments of a multiplexing converter are equivalent to the equivalent multiplicative noise and the equivalent additive noise, so as to allow evaluation of transmission impairments of the multiplexing converter, thereby to allow evaluation of the performance of a communication system. The multiplexing converter is a multiplexing analog-to-digital converter, or a multiplexing digital-to-analog converter. According to the present application, impairments of a multiplexing converter can be evaluated accurately, the performance evaluation of a communication system using the multiplexing converter is given, without being affected by an amplitude, a modulation format and a transmission rate of an input signal.

The application provides a composite component multi-source noise filtering method based on digital speckle, and aims at solving the technical problems of low measurement sensitivity and low precision of collected digital speckle images; the steps are as follows: firstly, digital speckle light paths are used to collect a digital speckle image before and after deformation of an object, phase information of the digital speckle image is extracted by using Fourier transform, and a composite component defect image is obtained; secondly, homomorphic transformation is performed on the composite component defect image, and multiplicative noise is converted into additive noise; then, the additive noise is filtered by using a compression sensing theory, and the multiplicative noise in the composite component defect image is filtered out; finally, a matrix singular value decomposition algorithm is used to filter out the additive noise in the composite component defect image, and a denoised component defect image is obtained; the K-SVD algorithm is used for denoising, details of the original image can be well recovered, Gaussian white noise can be removed, and the PSNR value of the denoised image is improved.

The invention belongs to the technical field of remote sensing image processing, and provides an SAR image denoising method and device based on a logarithm domain diffusion model. The denoising method comprises the following steps: preprocessing: converting an original SAR amplitude image to a logarithm domain to obtain an initial input image of a diffusion process; forward diffusion: constructing a forward diffusion path based on non-central Gaussian distribution, simulating a noise injection process, and generating a noise-containing image sequence; predicting noise through a neural network model: performing feature extraction and fusion on the noisy image and the physical metadata through the pre-constructed neural network model, and outputting a noise residual prediction result; and backward sampling reconstruction: carrying out iterative sampling by using a noise residual prediction result, and reconstructing a denoised SAR image. According to the denoising method, the problem of distribution mismatch of SAR multiplicative noise by a traditional diffusion model is solved, and image texture and structure information is reserved while the denoising precision is improved.

The invention discloses a target direction finding method based on expectation maximization in a multi-noise environment, and the method comprises the steps: constructing a noisy observation model y = diag (1 + e) a (w) x + n, e represents multiplicative noise, a (w) represents an array flow pattern vector, x represents signal intensity, and n represents additive noise; the expectation maximization algorithm and the optimization minimization algorithm are used for solving the frequency w and the intensity x of the signal for the noisy observation model. The method can jointly estimate the frequency and the intensity of the signal under the condition that additive noise and multiplicative noise exist; according to the method, the expected maximum value and the optimization minimization method are used for jointly estimating the frequency and the intensity, the estimation accuracy is high, and the convergence can be ensured.

The application discloses a synthetic aperture radar image speckle suppression method based on diffusion sampling, and relates to the field of synthetic aperture radar image processing. The method of the application is based on a sampling scheme of an approximate score function, reverses a transfer distribution, and applies a multiplicative noise model, thereby solving the problem of multiplicative noise degradation in a synthetic aperture radar image and avoiding supervised training and logarithmic transformation of a SAR image.

The invention discloses an intelligent detection system and method for welding defects of an electrolytic bath polar plate based on machine vision, and relates to the technical field of industrial vision detection. The method comprises the following steps: firstly, controlling an external heat source to carry out transient thermal excitation on an electrolytic cell polar plate welding seam, and synchronously acquiring high-resolution texture field data and time sequence thermal response field data on the surface of a polar plate; constructing a space-time covariance matrix by using an orthogonal heat flow projection analysis module, determining a background heat flow principal axis, generating a thermal anomaly residual image by calculating an orthogonal projection residual, and eliminating multiplicative noise caused by uneven surface emissivity from a physical level; the gradient direction included angle between the texture field and the thermal anomaly residual field is further calculated by using a heterogeneous gradient mutual exclusion judgment module, a heterogeneous gradient mutual exclusion correction factor is constructed, and the interference degree of the surface texture on thermal anomaly judgment is quantified. According to the method, the problems that the false alarm rate of the high-reflection metal surface is high and the subsurface defects are difficult to detect are effectively solved, and the detection robustness and accuracy are remarkably improved.

The application discloses an image multiplicative noise removing method and system, and relates to the technical field of image processing. The method comprises the following steps: constructing a multiplicative noise removing model combined with block matching local SVD operator-based sparse regularization and total variation and fractional variation combined regularization; decomposing the multiplicative noise removing model to obtain an optimization subproblem; the optimization subproblem comprises the following steps: sparse basis updating, a subproblem for solving a sparse regularization term, a subproblem for solving a total variation and fractional variation combined regularization term, and Lagrange multiplier updating; inputting a noise image into the multiplicative noise removing model, iteratively solving the optimization subproblem, and reconstructing a denoised image according to a solving result. The application can improve the quality and usability of an image.