Images Denoising by Improved Non-Local Means Algorithm

被引：0
|
作者：
He, Ning [1 ]
Lu, Ke [2 ]
机构：
[1] Beijing Union Univ, Sch Informat, Beijing 100101, Peoples R China
[2] Grad Univ Chinese Acad Sci, Coll Comp & Commun Engn, Beijing 100049, Peoples R China
来源：
THEORETICAL AND MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE | 2011年 / 164卷
关键词：
Image denoising; Non-Local means; Local Smoothing Filter;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A variety of methods have been introduced to remove noise from digital images. However, many algorithms remove the fine details and structure of the image in addition to the noise because of assumptions made about the frequency content of the image. The non-local means algorithm does not make these assumptions, but instead assumes that the image contains an extensive amount of redundancy. This work will implement the non-local means algorithm and compare it to other denoising methods in experimental results. The main focus of this paper is to propose an improved non-local means algorithm addressing the preservation of structure in a digital image. The NL-means algorithm is proven to be asymptotically optimal under a generic statistical image model. The powerful evaluation method to be the visualization of the method noise on natural images.
引用
收藏
页码：33 / +
页数：2
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