Parsimony and wavelet methods for denoising

Author

Krim, H. ; Pesquet, J.C. ; Schick, I.C.

Author_Institution

Stochastic Syst. Group, MIT, Cambridge, MA, USA

Volume

3

fYear

1998

fDate

12-15 May 1998

Firstpage

1869

Abstract

Some wavelet-based methods for signal estimation in the presence of noise are reviewed in the context of the parsimonious representation of the underlying signal. Three approaches are considered. The first is based on the application of the minimum description length (MDL) principle. The robustness of this method is improved in the second approach, by relaxing the assumption of known noise distribution following Huber´s (1967) work. In the third approach, a Bayesian strategy is adopted in order to incorporate prior information pertaining to the signal of interest; this method is especially useful at low signal-to-noise ratios

Keywords

Bayes methods; information theory; noise; parameter estimation; signal representation; statistical analysis; transform coding; wavelet transforms; Bayesian strategy; MDL principle; SNR; coding; denoising; information-theoretic methods; low signal-to-noise ratios; noise distribution; parsimonious representation; signal estimation; signal of interest; signal representation; wavelet methods; Bayesian methods; Estimation; Internetworking; Noise reduction; Noise robustness; Signal denoising; Signal processing; Signal to noise ratio; Stochastic systems; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681828

Filename

681828