Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25531
Title: Optimizing the multiwavelet shrinkage denoising
Authors: Hsung, TC
Lun, DPK 
Ho, KC
Keywords: Multiwavelet
Parameter estimation
Prefilter
Smoothing methods
Wavelet transforms
White noise
Issue Date: 2005
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on signal processing, 2005, v. 53, no. 1, p. 240-251 How to cite?
Journal: IEEE transactions on signal processing 
Abstract: Denoising methods based on wavelet domain thresholding or shrinkage have been found to be effective. Recent studies reveal that multivariate shrinkage on multiwavelet transform coefficients further improves the traditional wavelet methods. It is because multiwavelet transform, with appropriate initialization, provides better representation of signals so that their difference from noise can be clearly identified. In this paper, we consider the multiwavelet denoising by using multivariate shrinkage function. We first suggest a simple second-order orthogonal prefilter design method for applying multiwavelet of higher multiplicities. We then study the corresponding thresholds selection using Stein's unbiased risk estimator (SURE) for each resolution level provided that we know the noise structure. Simulation results show that higher multiplicity wavelets usually give better denoising results and the proposed threshold estimator suggests good indication for optimal thresholds.
URI: http://hdl.handle.net/10397/25531
ISSN: 1053-587X
EISSN: 1941-0476
DOI: 10.1109/TSP.2004.838927
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