Anisotropic nonlocal means denoising

被引：29

作者：

Maleki, Arian ^{[1
]}

Narayan, Manjari ^{[1
]}

Baraniuk, Richard G. ^{[1
]}

机构：

[1] Rice Univ, Dept Comp & Elect Engn, Houston, TX 77005 USA

来源：

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS | 2013年 / 35卷 / 03期

关键词：

Denoising; Non local means; Minimax risk; Anisotropy; IMAGE; REPRESENTATIONS; COMPRESSION; TRANSFORM; SURFACES; TEXTURE; EDGES;

D O I：

10.1016/j.acha.2012.11.003

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

It has recently been proved that the popular nonlocal means (NLM) denoising algorithm does not optimally denoise images with sharp edges. Its weakness lies in the isotropic nature of the neighborhoods it uses to set its smoothing weights. In response, in this paper we introduce several theoretical and practical anisotropic nonlocal means (ANLM) algorithms and prove that they are near minimax optimal for edge-dominated images from the Horizon class. On real-world test images, an ANLM algorithm that adapts to the underlying image gradients outperforms NLM by a significant margin. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：452 / 482

页数：31

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