12 results about "Multiplicative noise" patented technology

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 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 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 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.

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 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 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.