New time-scale adaptive denoising method based on wavelet shrinkage

Xiao Ping Zhang, Zhi Quan Luo

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations

Abstract

The wavelet shrinkage denoising approach is able to maintain local regularity of a signal while suppressing noise. However, the conventional wavelet shrinkage based methods are not time-scale adaptive to track the local time-scale variation. In this paper, a new time-scale adaptive denoising method for deterministic signal estimation is presented, based on the wavelet shrinkage. A class of smooth shrinkage functions and the local SURE (Stein's Unbiased Risk Estimate) risk are employed to achieve time-scale adaptive denoising. The system structure and the learning algorithm are developed. The numerical results of our system are presented and compared with the conventional wavelet shrinkage techniques as well as their optimal solutions. Results indicate that the new time-scale adaptive method is superior to the conventional methods. It is also shown that the new method sometimes even achieves better performance than the optimal solution of the conventional wavelet shrinkage techniques.

Original languageEnglish (US)
Pages (from-to)1629-1632
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
StatePublished - Jan 1 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: Mar 15 1999Mar 19 1999

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