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A One-Dimensional Signal Denoising Enhancement Method Based on Nonlocal Similarity

A non-local similarity and non-local similarity technology, which is applied in the field of one-dimensional signal denoising and enhancement based on non-local similarity, can solve the problems that there is no better solution and the number of network hidden layer nodes is difficult to determine

Active Publication Date: 2020-12-18
XIAN UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Neural network denoising requires a large number of training samples, and the number of network hidden layer nodes is difficult to determine
The disadvantage of Kalman filter and particle filter is that it requires known system model and noise statistical characteristics
How to enhance the denoising ability of the algorithm based on the original denoising method, improve the signal-to-noise ratio of the denoising signal, and reduce the mean square error, there is still no better solution

Method used

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  • A One-Dimensional Signal Denoising Enhancement Method Based on Nonlocal Similarity
  • A One-Dimensional Signal Denoising Enhancement Method Based on Nonlocal Similarity
  • A One-Dimensional Signal Denoising Enhancement Method Based on Nonlocal Similarity

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Embodiment Construction

[0060] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0061] The present invention is a one-dimensional signal denoising enhancement method based on non-local similarity, which is specifically implemented according to the following steps:

[0062] Step 1: Denoise the observed signal f to obtain a denoised signal

[0063] Suppose the observed signal f=[f(1),f(2),…,f(k),…,f(N)]

[0064] Among them, f(k)=s(k)+n(k)k=0,1,2,...,N-1

[0065] f(k) is the observed signal, s(k) is the original signal, v(k) is the noise signal, k is the time, 1≤k≤N, and N is the signal length.

[0066] Select an existing denoising method, such as mean filtering, median filtering, Gaussian filtering, wavelet denoising, principal component analysis denoising, sparse representation denoising, etc., and perform denoising processing to obtain a denoising signal Take the wavelet threshold denoising method as an example to ...

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Abstract

The invention discloses a one-dimensional signal denoising and enhancement method based on a nonlocal similarity. The method is implemented specifically according to the following steps that: S1: carrying out denoising processing on an observation signal f to obtain a primary denoising signal; S2: carrying out fragment division on the primary denoising signal obtained in the 1, and calculating the secondary denoising result of a signal fragment according to the nonlocal similarity; and S3: on the basis of the secondary denoising result, which is obtained in the S2, of the signal fragment, carrying out fusion normalization processing on the secondary denoising result to obtain the denoising and enhancement result of the observation signal f. By use of the one-dimensional signal denoising and enhancement method based on the nonlocal similarity, a signal denoising effect can be further improved.

Description

technical field [0001] The invention belongs to the technical field of digital signal processing, and in particular relates to a non-local similarity-based one-dimensional signal denoising enhancement method. Background technique [0002] In the process of signal acquisition and transmission, due to the interference of the external environment, the influence of the transmission medium and the instrument itself, it is inevitable that there will be noise mixed in it, and noise is an important factor affecting the performance of the target signal. In some high-precision data analysis and processing, even weak noise will have a huge impact on the analysis results, especially when the original signal is relatively weak, the noise will seriously damage the characteristics of the real signal, how to effectively remove and suppress the noise has very important meaning. [0003] At present, the denoising methods mainly include Fourier transform denoising, wavelet transform denoising...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/14G06F17/16
CPCG06F17/148G06F17/16
Inventor 谢国杜许龙钱富才张永艳黑新宏
Owner XIAN UNIV OF TECH