基于泊松分布的非局部均值图像去噪方法

高晓玲

doi:10.37188/YJYXS20203510.1059

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基于泊松分布的非局部均值图像去噪方法

显示与图像处理 | 更新时间：2020-09-28

- 基于泊松分布的非局部均值图像去噪方法
- Nonlocal mean image denoising method based on Poisson distribution
- 液晶与显示 2020年35卷第10期页码：1059-1065
- 作者机构：
  
  宁夏大学新华学院, 宁夏银川 750021
- 作者简介：
  
  [ "高晓玲(1981-), 女, 宁夏平罗人, 硕士, 讲师, 2007年于西安科技大学获得硕士学位, 主要从事信号及图像处理方面的研究。E-mail:sunnyday_xxl@163.com" ]
- 基金信息：
  
  宁夏高等学校科学技术研究项目(NGY2020102)
- DOI：10.37188/YJYXS20203510.1059
  中图分类号： TP753
- 收稿日期：2020-04-23，
  
  修回日期：2020-05-20，
  
  录用日期：2020-5-20，
  
  纸质出版日期：2020-10-05
- 稿件说明：
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高晓玲. 基于泊松分布的非局部均值图像去噪方法[J]. 液晶与显示, 2020,35(10):1059-1065.

Xiao-ling GAO. Nonlocal mean image denoising method based on Poisson distribution[J]. Chinese journal of liquid crystals and displays, 2020, 35(10): 1059-1065.
高晓玲. 基于泊松分布的非局部均值图像去噪方法[J]. 液晶与显示, 2020,35(10):1059-1065. DOI： 10.37188/YJYXS20203510.1059.

Xiao-ling GAO. Nonlocal mean image denoising method based on Poisson distribution[J]. Chinese journal of liquid crystals and displays, 2020, 35(10): 1059-1065. DOI： 10.37188/YJYXS20203510.1059.

摘要

针对图像去噪问题，提出基于泊松分布的非局部均值图像去噪方法。将图像中的每一个像素点建模为一个泊松分布，泊松分布的参数根据像素点非局部区域内像素值信息进行极大似然估计；两像素间的差异性由其对应泊松分布的L2范数距离计算；同时，利用两像素点邻域内点对间泊松分布的L2范数的平方和来定义其相似性权值，采用非局部均值的思想进行图像泊松去噪。实验结果表明，所提算法能很好地保留图像中的细节信息，去噪图像峰值信噪比达到22 dB以上，能够有效用于图像去噪。

Abstract

In order to handle the image denoising problem

a non-local mean image denoising method based on Poisson distribution is proposed in this paper. Each pixel in image is modeled as a poisson distribution. The parameters of Poisson distribution are estimated according to the maximum likelihood of the pixels in a non-local region. The difference between two pixels is calculated by the L2 norm distance corresponding to the Poisson distribution. Moreover

the similarity weights are defined by the sum of L2 norms between two pixels in their neighborhood points. The image Poisson denoising is carried out by using the principle of non-local mean. The experimental results show that the proposed algorithm can preserve the image details well

and the denoised image has a high peak signal-to-noise rate over 22 dB. The proposed method can be effectively used for image denoising.

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references

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