On optimal threshold selection for multiwavelet shrinkage

Tai Chiu Hsung, Pak Kong Lun

Research output: Journal article publicationConference articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Recent researches found 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 optimal threshold selection for multiwavelet denoising by using multivariate shrinkage function. Firstly, we study the threshold selection using the Stein's unbiased risk estimator (SURE) for each, resolution level when the noise structure is given. Then, we consider the method of generalized cross validation (GCV) when the noise structure is not known a priori. Simulation results show that the higher multiplicity (>2) wavelets usually give better denoising results. Besides, the proposed threshold estimators often suggest better thresholds as compared with the traditional estimators.
Original languageEnglish
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
Publication statusPublished - 7 Oct 2004
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 17 May 200421 May 2004

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

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