Hidden Markov Models for Wavelet-based Signal Processing

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameAsilomar Conference on Signals, Systems, and Computersen_US
dc.citation.firstpage1029en_US
dc.citation.lastpage1035en_US
dc.citation.locationPacific Grove, CAen_US
dc.citation.volumeNumber2en_US
dc.contributor.authorCrouse, Matthewen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorNowak, Robert Daviden_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:40:41Zen_US
dc.date.available2007-10-31T00:40:41Zen_US
dc.date.issued1996-11-01en_US
dc.date.modified2006-06-12en_US
dc.date.note2006-06-12en_US
dc.date.submitted1996-11-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractCurrent 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.en_US
dc.identifier.citationM. Crouse, R. G. Baraniuk and R. D. Nowak, "Hidden Markov Models for Wavelet-based Signal Processing," vol. 2, 1996.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/ACSSC.1996.599100en_US
dc.identifier.urihttps://hdl.handle.net/1911/19812en_US
dc.language.isoengen_US
dc.subject.otherDSP for Communicationsen_US
dc.titleHidden Markov Models for Wavelet-based Signal Processingen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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