Image denoising using principal component analysis in the wavelet domain

Authors:
Silvia Bacchelli;Serena Papi
Affiliations:
Department of Mathematics, University of Bologna, via Sacchi 3, 47023 Cesena, Italy;Department of Mathematics, University of Bologna, via Sacchi 3, 47023 Cesena, Italy
Venue:
Journal of Computational and Applied Mathematics
Year:
2006

Citing 7
Cited 1

Ten lectures on wavelets

Ten lectures on wavelets
An introduction to wavelets

An introduction to wavelets
Generalized cross validation for wavelet thresholding

Signal Processing
Digital Image Processing

Digital Image Processing
Filtered wavelet thresholding methods

Journal of Computational and Applied Mathematics - Special Issue: Proceedings of the 10th international congress on computational and applied mathematics (ICCAM-2002)
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way

A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Adaptive wavelet thresholding for image denoising and compression

IEEE Transactions on Image Processing

Wavelet-based compression with ROI coding support for mobile access to DICOM images over heterogeneous radio networks

IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics

Quantified Score

Hi-index	7.29

Visualization

Abstract

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.