基于统计特性的非局部均值去噪算法

陈明举

doi:10.3788/YJYXS20142903.0450

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基于统计特性的非局部均值去噪算法

成像技术与图像处理 | 更新时间：2020-08-12

- 基于统计特性的非局部均值去噪算法
- Non-local means image denoising algorithm based on statistical property
- 液晶与显示 2014年29卷第3期页码：450-454
- 作者机构：
  
  四川理工学院自动化与电子信息学院, 四川自贡 643000
- 作者简介：
  
  [ "陈明举（1982-），男，重庆大足人，讲师，主要从事通信信息处理方面的研究。E-mail：chenmingju@live.cn" ]
- 基金信息：
  
  人工智能四川省重点实验室开放基金（No.2012RYY08）();四川理工学院校级科研基金（No.2012KY13）();四川省教育厅项目（No.14ZB0211）(
- DOI：10.3788/YJYXS20142903.0450
  中图分类号： TN911.73
- 收稿日期：2013-07-24，
  
  修回日期：2013-10-09，
  
  网络出版日期：2013-10-15，
  
  纸质出版日期：2014-04-25
- 稿件说明：
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陈明举. 基于统计特性的非局部均值去噪算法[J]. 液晶与显示, 2014,29(3): 450-454

CHEN Ming-ju. Non-local means image denoising algorithm based on statistical property[J]. , 2014,29(3): 450-454
陈明举. 基于统计特性的非局部均值去噪算法[J]. 液晶与显示, 2014,29(3): 450-454 DOI： 10.3788/YJYXS20142903.0450.

CHEN Ming-ju. Non-local means image denoising algorithm based on statistical property[J]. , 2014,29(3): 450-454 DOI： 10.3788/YJYXS20142903.0450.

摘要

针对非局部均值滤波的权值由相似块的欧式距来确定而未考虑其受噪声影响的缺点，提出了一种权值由相似块欧式距的统计特性确定的去噪算法。该算法首先对受到高斯噪声干扰的图像相似块的欧式距建立概率分布函数，再由概率分布函数确定权值大小，从而有效地减小高斯噪声对加权系数的影响，以提高去噪性能。实验中，从主客观方面与传统非局部均值滤波进行对比分析，实验数据表明本文提出的算法峰值信噪比提高约1 dB，去除噪声的同时保留更多图像的细节信息，去噪性能更优。

Abstract

In order to overcome the shortcomings that weights of traditional non-local means are only decided by similar block's Euclidean distance and the influence of noise is not considered

a novel non-local means algorithm based on statistical property of similar block's pixels is proposed. This new algorithm first obtains the probability density function of similar block's Euclidean distance by employing the prior information of the Gaussian noise

and then formulates the probabilistic weights truly reflecting the similarity between two noisy patches. In the experiments

compared with traditional non-local means algorithm from the subjective and objective

this new algorithm gains 1 dB higher peak signal noise ratio than the traditional non-local means algorithm

and removes the noise more effectively while preserving significant image details.

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references

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