Noise reduction and edge detection via kernel anisotropic diffusion

被引：98

作者：

Yu, Jinhua ^{[1
]}

Wang, Yuanyuan ^{[1
]}

Shen, Yuzhong ^{[2
]}

机构：

[1] Fudan Univ, Dept Elect Engn, Shanghai 200433, Peoples R China

[2] Old Dominion Univ, Dept Elect & Comp Engn, Norfolk, VA 23529 USA

来源：

PATTERN RECOGNITION LETTERS | 2008年 / 29卷 / 10期

基金：

中国国家自然科学基金;

关键词：

anisotropic diffusion; kernel methods; noise reduction; edge detection;

D O I：

10.1016/j.patrec.2008.03.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A novel kernel anisotropic diffusion (KAD) method is proposed for robust noise reduction and edge detection. The KAD incorporates a kernelized gradient operator in the diffusion, leading to more effective edge detection and providing a better control to the diffusion process. Adaptive diffusion threshold estimation and automatic diffusion termination criterion are also introduced to enhance the robustness of the KAD. The KAD outperforms several previous anisotropic diffusion-based methods for low SNR images. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：1496 / 1503

页数：8

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