Asymptotic Non-local Means Image Denoising Algorithm

被引：0
|
作者：
Xing X.-X. [1 ]
Wang H.-L. [2 ]
Li J. [1 ]
Zhang X.-D. [1 ]
机构：
[1] School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an
[2] School of Mathematics and Computer Science, Ningxia Normal University, Guyuan
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2020年 / 46卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Asymptotic non-local means (ANLM); Fast algorithm; Image denoising; Non-local means (NLM);
D O I：
10.16383/j.aas.c190294
中图分类号：
学科分类号：
摘要：
Non-local means denoising (NLM) algorithm is a milestone algorithm in the field of image processing. The proposal of NLM has opened up the non-local method which has a deep influence. This paper performed a revisit for NLM from two aspects as follows: 1) To alleviate the high computational complexity problem of NLM, a fast algorithm was constructed, which was based on cross-correlation and fast Fourier transform; 2) NLM always blur structures and textures during the noise removal, especially in the case of strong noise. To solve this problem, an asymptotic non-local means (ANLM) image denoising algorithm is put forward, which uses the property of noise variance to control the filtering parameters. Numerical experiments illustrate that the fast algorithm is 27 times faster than classical implementation with standard parameter configuration, and the ANLM obtain better denoising effect than classical NLM. Copyright © 2020 Acta Automatica Sinica. All rights reserved.
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页码：1952 / 1960
页数：8
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