Signal estimation using wavelet-Markov models

Author

Crouse, Matthew S. ; Baraniuk, Richard G. ; Nowak, Robert D.

Author_Institution

Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA

Volume

5

fYear

1997

fDate

21-24 Apr 1997

Firstpage

3429

Abstract

Current wavelet-based statistical signal and image processing techniques such as shrinkage and filtering treat the wavelet coefficients as though they were statistically independent. This assumption is unrealistic; considering the statistical dependencies between wavelet coefficients can yield substantial performance improvements. We develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet coefficients. To illustrate the power of the new framework, we derive a new algorithm for signal estimation in nonGaussian noise

Keywords

hidden Markov models; parameter estimation; signal processing; statistical analysis; wavelet transforms; white noise; additive white nonGaussian noise; algorithm; filtering; hidden Markov models; image processing; shrinkage; signal estimation; statistical dependencies; statistical signal; wavelet based signal processing; wavelet coefficients; wavelet-Markov models; Atomic measurements; Estimation; Frequency; Hidden Markov models; Image coding; Image processing; Prototypes; Signal processing; Wavelet coefficients; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.604601

Filename

604601