Statistical signal processing usin wavelet-domain hidden markov models

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

doi:10.1117/12.279689

Statistical signal processing usin wavelet-domain hidden markov models

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

Houston Methodist

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Most wavelet-based statistical signal and image processing techniques 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. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Pages	248-259
Number of pages	12
Volume	3169
DOIs	https://doi.org/10.1117/12.279689
State	Published - 1997
Event	Wavelet Applications in Signal and Image Processing V - San Diego, CA, United States Duration: Jul 30 1997 → Jul 30 1997

Other

Other	Wavelet Applications in Signal and Image Processing V
Country/Territory	United States
City	San Diego, CA
Period	7/30/97 → 7/30/97

Keywords

Estimation
Hidden markov models
Wavelets

ASJC Scopus subject areas

Applied Mathematics
Computer Science Applications
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials
Condensed Matter Physics

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10.1117/12.279689

Cite this

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title = "Statistical signal processing usin wavelet-domain hidden markov models",

abstract = "Most wavelet-based statistical signal and image processing techniques 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. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.",

keywords = "Estimation, Hidden markov models, Wavelets",

author = "Crouse, {Matthew S.} and Nowak, {Robert D.} and Baraniuk, {Richard G.}",

year = "1997",

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language = "English (US)",

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T1 - Statistical signal processing usin wavelet-domain hidden markov models

AU - Crouse, Matthew S.

AU - Nowak, Robert D.

AU - Baraniuk, Richard G.

PY - 1997

Y1 - 1997

N2 - Most wavelet-based statistical signal and image processing techniques 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. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.

AB - Most wavelet-based statistical signal and image processing techniques 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. In this paper, we develop a new framework for wavelet-based signal processing that employs hidden Markov models to characterize the dependencies between wavelet, coefficients.

KW - Estimation

KW - Hidden markov models

KW - Wavelets

UR - http://www.scopus.com/inward/record.url?scp=0005499124&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0005499124&partnerID=8YFLogxK

U2 - 10.1117/12.279689

DO - 10.1117/12.279689

M3 - Conference contribution

AN - SCOPUS:0005499124

VL - 3169

SP - 248

EP - 259

BT - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Wavelet Applications in Signal and Image Processing V

Y2 - 30 July 1997 through 30 July 1997

ER -

Statistical signal processing usin wavelet-domain hidden markov models

Abstract

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Keywords

ASJC Scopus subject areas

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