Hidden Markov Models for Wavelet-based Signal Processing
| dc.citation.bibtexName | inproceedings | en_US |
| dc.citation.conferenceName | Asilomar Conference on Signals, Systems, and Computers | en_US |
| dc.citation.firstpage | 1029 | |
| dc.citation.lastpage | 1035 | |
| dc.citation.location | Pacific Grove, CA | en_US |
| dc.citation.volumeNumber | 2 | en_US |
| dc.contributor.author | Crouse, Matthew | en_US |
| dc.contributor.author | Baraniuk, Richard G. | en_US |
| dc.contributor.author | Nowak, Robert David | en_US |
| dc.contributor.org | Digital Signal Processing (http://dsp.rice.edu/) | en_US |
| dc.date.accessioned | 2007-10-31T00:40:41Z | |
| dc.date.available | 2007-10-31T00:40:41Z | |
| dc.date.issued | 1996-11-01 | en |
| dc.date.modified | 2006-06-12 | en_US |
| dc.date.note | 2006-06-12 | en_US |
| dc.date.submitted | 1996-11-01 | en_US |
| dc.description | Conference Paper | en_US |
| 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. | en_US |
| dc.identifier.citation | M. Crouse, R. G. Baraniuk and R. D. Nowak, "Hidden Markov Models for Wavelet-based Signal Processing," vol. 2, 1996. | |
| dc.identifier.doi | http://dx.doi.org/10.1109/ACSSC.1996.599100 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/19812 | |
| dc.language.iso | eng | |
| dc.subject.other | DSP for Communications | en_US |
| dc.title | Hidden Markov Models for Wavelet-based Signal Processing | en_US |
| dc.type | Conference paper | |
| dc.type.dcmi | Text |
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