Threshold self-adaptive optimizing de-noising method based on wavelet transform

NIU Hongxia; ZHANG Zhaoxin; NING Zheng; CHEN Guangwu

doi:10.13873/j.1000-9787(2020)02-0033-04

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Threshold self-adaptive optimizing de-noising method based on wavelet transform

更新时间：2025-09-11

- Threshold self-adaptive optimizing de-noising method based on wavelet transform
- Issue 2, Pages: 33-36(2020)
- 作者机构：
  
  1. 兰州交通大学自动控制研究所
  2. 甘肃省高原交通信息工程及控制重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2020)02-0033-04
  CLC： TP391.41
- Published：2020
- 稿件说明：
移动端阅览
NIU Hongxia, ZHANG Zhaoxin, NING Zheng, et al. Threshold self-adaptive optimizing de-noising method based on wavelet transform[J]. 2020, (2): 33-36.
DOI：

NIU Hongxia, ZHANG Zhaoxin, NING Zheng, et al. Threshold self-adaptive optimizing de-noising method based on wavelet transform[J]. 2020, (2): 33-36. DOI： 10.13873/j.1000-9787(2020)02-0033-04.

摘要

针对小波阈值去噪算法中图像信息与噪声混叠造成的去噪信号模糊问题

提出了一种基于小波变换的阈值自适应寻优去噪方法。一方面考虑图像的边缘特征

引入比例萎缩因子

使图像有用信息和噪声信息分离;另一方面改进Bayes Shrink阈值函数

通过粒子群优化(PSO)算法自适应地寻找噪声图像的最优阈值。针对小波分解层之间噪声的不同

对阈值函数进行改进

根据不同方向和不同尺度上噪声的分布自适应去噪。对一维信号和二维信号进行去噪实验

与现有文献方法相比

本方法能够有效去除噪声

更好地保留原信号中的有用信息。

Abstract

Aiming at the problem of de-noising signal blur caused by image information and noise aliasing in wavelet threshold de-noising algorithm

a threshold adaptive optimizing de-noising algorithm based on wavelet transform is proposed. On the one hand

considering the edge features of the image

the proportional shrinkage factor is introduced to separate the useful information of the image from the noise information. On the other hand

the Bayes Shrink threshold function is improved

and the optimal threshold of the noise image is adaptively searched by the PSO algorithm. The threshold function is improved for the difference of noise between wavelet decomposition layers

and adaptive de-noising is performed according to the distribution of noise in different directions and different scales. The de-noising experiment is performed on the one-dimensional signal and the twodimensional signal. Compared with the existing literature methods

the method can effectively remove the noise and better preserve the useful information in the original signal.

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