Signal Estimation using Wavelet-Markov Models

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)en_US
dc.citation.firstpage3429
dc.citation.lastpage3432
dc.citation.locationMunich, Germanyen_US
dc.citation.volumeNumber5en_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.accessioned2007-10-31T00:40:44Z
dc.date.available2007-10-31T00:40:44Z
dc.date.issued1997-04-01en
dc.date.modified2006-06-12en_US
dc.date.note2006-06-12en_US
dc.date.submitted1997-04-01en_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. 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.en_US
dc.identifier.citationM. Crouse, R. G. Baraniuk and R. D. Nowak, "Signal Estimation using Wavelet-Markov Models," vol. 5, 1997.
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.1997.604601en_US
dc.identifier.urihttps://hdl.handle.net/1911/19813
dc.language.isoeng
dc.subject.otherDSP for Communicationsen_US
dc.titleSignal Estimation using Wavelet-Markov Modelsen_US
dc.typeConference paper
dc.type.dcmiText
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