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dc.contributor.authorCrouse, Matthew
Baraniuk, Richard G.
Nowak, Robert David
dc.creatorCrouse, Matthew
Baraniuk, Richard G.
Nowak, Robert David 2007-10-31T00:40:41Z 2007-10-31T00:40:41Z 1996-11-01 1996-11-01
dc.description Conference Paper
dc.description.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. 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. To illustrate the power of the new framework, we derive a new signal denoising algorithm that outperforms current scalar shrinkage techniques.
dc.language.iso eng
dc.subject.otherDSP for Communications
dc.title Hidden Markov Models for Wavelet-based Signal Processing
dc.type Conference paper 2006-06-12
dc.citation.bibtexName inproceedings 2006-06-12
dc.contributor.orgDigital Signal Processing (
dc.citation.volumeNumber 2
dc.citation.location Pacific Grove, CA
dc.citation.conferenceName Asilomar Conference on Signals, Systems, and Computers
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doi 10.1109/ACSSC.1996.599100
dc.citation.firstpage 1029
dc.citation.lastpage 1035
dc.identifier.citation M. Crouse, R. G. Baraniuk and R. D. Nowak, "Hidden Markov Models for Wavelet-based Signal Processing," vol. 2, 1996.

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  • ECE Publications [1153]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.

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