Image denoising using principal component analysis in the wavelet domain

被引：16

作者：

Bacchelli, S ^{[1
]}

Papi, S ^{[1
]}

机构：

[1] Univ Bologna, Dept Math, I-47023 Cesena, Italy

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2006年 / 189卷 / 1-2期

关键词：

wavelet packets; KL transform; filter banks; recursive matrices; image denoising;

D O I：

10.1016/j.cam.2005.04.030

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this work we describe a method for removing Gaussian noise from digital images, based on the combination of the wavelet packet transform and the principal component analysis. In particular, since the aim of denoising is to retain the energy of the signal while discarding the energy of the noise, our basic idea is to construct powerful tailored filters by applying the Karhunen-Loeve transform in the wavelet packet domain, thus obtaining a compaction of the signal energy into a few principal components, while the noise is spread over all the transformed coefficients. This allows us to act with a suitable shrinkage function on these new coefficients, removing the noise without blurring the edges and the important characteristics of the images. The results of a large numerical experimentation encourage us to keep going in this direction with our studies. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：606 / 621

页数：16

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