基于改进的小波阈值图像去噪算法

贾文良; 陈雨; 陈强

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基于改进的小波阈值图像去噪算法

更新时间：2026-09-04

- 基于改进的小波阈值图像去噪算法
- Image denoising algorithm based on improved wavelet threshold
- 微电子学与计算机 2020年37卷第10期页码：24-29
- 作者机构：
  
  四川大学电子信息学院智能控制研究所,四川,成都,610065
- 作者简介：
- 基金信息：
  
  国家973计划项目 2013CB328903-2
- DOI：
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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贾文良, 陈雨, 陈强. 基于改进的小波阈值图像去噪算法[J]. 微电子学与计算机, 2020, 37(10): 24-29.

JIA Wen-liang, CHEN Yu, CHEN Qiang. Image denoising algorithm based on improved wavelet threshold[J]. 2020, 37(10): 24-29.
贾文良, 陈雨, 陈强. 基于改进的小波阈值图像去噪算法[J]. 微电子学与计算机, 2020, 37(10): 24-29. DOI：

JIA Wen-liang, CHEN Yu, CHEN Qiang. Image denoising algorithm based on improved wavelet threshold[J]. 2020, 37(10): 24-29. DOI：

摘要

为了克服传统小波阈值函数存在固定偏差和不连续的问题，提出了一种改进的小波阈值函数，将改进的小波阈值方法和软阈值、硬阈值、Birge-Massart策略软阈值、半软阈值、半软阈值组合均值滤波等方法比较，峰值信噪比提高了1.2%~6.9%，均方根误差减小了4.8%~22.4%.在混有高斯噪声、椒盐噪声和斑点噪声的图像中，改进的小波阈值函数和自适应中值滤波组合的方法较单一的自适应中值滤波，峰值信噪比提高3 dB，均方根误差减小27%.结果表明，该算法有效的提升了高斯噪声图像的去噪效果，而且组合去噪方法对图像中的混合噪声去除效果较好.

Abstract

In order to overcome the problems of fixed deviation and discontinuity of the traditional wavelet threshold function

an improved wavelet threshold function is proposed. Compared with soft threshold method

hard threshold method

Birge MassArt strategy soft threshold method

half soft threshold method and half soft threshold combined mean filtering method

the peak signal-to-noise ratio is increased by 1.2% ~ 6.9%

and the root mean square error is reduced by 4.8% ~ 22.4%. In the image mixed with Gaussian noise

salt and pepper noise and speckle noise

the combination method of improved wavelet threshold function and adaptive median filter is better than the single adaptive median filter

the peak signal-to-noise ratio is increased by 3 dB

and the root mean square error is reduced by 27%. The results show that the algorithm effectively improves the denoising effect of Gaussian noise image

and the combined denoising method has a better effect on the removal of mixed noise in the image.

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references

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ZHOU X F, ZHU W W, GUO Q G. The denoising of ultrasonic signal based on asymptotic semi-soft thresholding function[J]. Journal of Detection % Control, 2011, 33(2): 35-39. DOI: 35

GUO X K, JIAN T, DONG Y L. Radar one-dimensional range profile recognition based on improved wavelet denoising [J]. Radar Science and Technology, 2019, 17(4):360-364+370. DOI: 360

CHEN Z A, HU Z F. Remote sensing image denoising based on improved wavelet threshold algorithm[J]. Bulletin of Surveying and Mapping, 2018(4): 28-31. DOI: 28

WANG Q, CHENG B, DU J, et al. An improved method for image denoising based on wavelet thresholding[J]. Computer and Modernization, 2015 (4): 65-69. DOI: 65

ZHANG Z F, WEI H, TAN B W.An improved wavelet threshold denoising method[J].Study on Optical Communications, 2018(2):75-78. DOI: 75

CUI J G, CHEN B Q, XU Q, et al. A wavelet threshold image denoising algorithm based on a new kind of sign function[J]. Telecommunications Science, 2017, 33(1): 45-52. DOI: 45

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