Rotationally invariant wavelet shrinkage

被引：0
|
作者：
Mrázek, P [1 ]
Weickert, J [1 ]
机构：
[1] Univ Saarland, Fac Math & Comp Sci, Math Image Anal Grp, D-66123 Saarbrucken, Germany
来源：
PATTERN RECOGNITION, PROCEEDINGS | 2003年 / 2781卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Most two-dimensional methods for wavelet shrinkage are efficient for edge-preserving image denoising, but they suffer from poor rotation invariance. We address this problem by designing novel shrinkage rules that are derived from rotationally invariant nonlinear diffusion filters. The resulting Haar wavelet shrinkage methods are computationally inexpensive and they offer substantially improved rotation invariance.
引用
收藏
页码：156 / 163
页数：8
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