Adaptive nonlocal filtering: A fast alternative to anisotropic diffusion for image enhancement

被引：46
作者：
Fischl, B
Schwartz, EL
机构：
[1] Boston Univ, Dept Cognit & Neural Syst, Boston, MA 02215 USA
[2] Massachusetts Gen Hosp, NMR Ctr, Charlestown, MA 02129 USA
来源：
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 1999年 / 21卷 / 01期
基金：
美国国家科学基金会;
关键词：
segmentation; diffusion; scale-space; anisotropic diffusion; nonlinear diffusion; filtering; permutation filter; nonlocal filter;
D O I：
10.1109/34.745732
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Nonlinear anisotropic diffusion algorithms provide significant improvement in image enhancement as compared to linear filters. However, the excessive computational cost of solving nonlinear PDEs precludes their use in real-time vision applications. In the present paper, we show that two orders of magnitude speed improvement is provided by a new image filtering paradigm in which an adaptively determined vector field specifies nonlocal application points for an image filter.
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页码：42 / 48
页数：7
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