Multiple wavelet threshold estimation by generalized cross validation for images with correlated noise

被引：67

作者：

Jansen, M ^{[1
]}

Bultheel, A ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Dept Comp Sci, B-3001 Heverlee, Belgium

来源：

IEEE TRANSACTIONS ON IMAGE PROCESSING | 1999年 / 8卷 / 07期

关键词：

correlated noise; cross validation; noise reduction; thresholding; wavelets;

D O I：

10.1109/83.772237

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Denoising algorithms based on wavelet thresholding replace small wavelet coefficients by zero and keep or shrink the coefficients with absolute value above the threshold. The optimal threshold minimizes the error of the result as compared to the unknown, exact data. To estimate this optimal threshold, we use generalized cross validation. This procedure does not require an estimation for the noise energy. Originally, this method assumes uncorrelated noise. In this paper, we describe how we can extend it to images with correlated noise.

引用

页码：947 / 953

页数：7

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