A Two-Step Regularization Framework for Non-Local Means

As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computin...

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Bibliographic Details
Published inJournal of computer science and technology Vol. 29; no. 6; pp. 1026 - 1037
Main Author 孙忠贵 陈松灿 乔立山
Format Journal Article
LanguageEnglish
Published Boston Springer US 01.11.2014
Springer Nature B.V
Subjects
Artificial Intelligence
Communities
Computer Science
Data Structures and Information Theory
Image processing
Information Systems Applications (incl.Internet)
Least squares method
NLM
Noise
Noise reduction
Optimization
Platforms
Regular Paper
Regularization
Software Engineering
Theory of Computation
中值滤波器
去噪方法
图像处理
最小二乘问题
框架
正规化
降噪性能
non-local means
framework
non-local median
image denoising
regularization
Online AccessGet full text
ISSN1000-9000
1860-4749
DOI10.1007/s11390-014-1487-9

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Summary:As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the fl-amework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results.
Bibliography:11-2296/TP
As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the fl-amework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results.
non-local means, non-local median, framework, image denoising, regularization
Zhong-Gui Sun Song-Can Chen and Li-Shan Qiao（1Department of Mathematics Science, Liaocheng University, Liaocheng 252000, China 2 College of Computer Science and Technology, Nanjing University of Aeronautics ~z Astronautics, Nanjing 210016, China）
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ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-014-1487-9