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 language | English |
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| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Volume | 2 |
| Publication status | Published - 7 Oct 2004 |
| Event | Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada Duration: 17 May 2004 → 21 May 2004 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Signal Processing
- Acoustics and Ultrasonics