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

作　　者：高晓玲[1] GAO Xiao-ling(College of Xinhua, Ningxia University, Yinchuan 750021, China)

机构地区：[1]宁夏大学新华学院,宁夏银川750021

出　　处：《液晶与显示》2020年第10期1059-1065,共7页Chinese Journal of Liquid Crystals and Displays

基　　金：宁夏高等学校科学技术研究项目(No.NGY2020102)。

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。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.

关 键 词：泊松去噪 L2范数 泊松分布 非局部均值 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]