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    Hidden Markov Models for Wavelet-based Signal Processing

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    Author
    Crouse, Matthew; Baraniuk, Richard G.; Nowak, Robert David
    Date
    2006-06-12
    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.
    Description
    Conference Paper
    Citation
    M. Crouse, R. G. Baraniuk and R. D. Nowak, "Hidden Markov Models for Wavelet-based Signal Processing," vol. 2, 1996.
    Published Version
    http://dx.doi.org/10.1109/ACSSC.1996.599100
    Keyword
    DSP for Communications
    Type
    Conference paper
    Citable link to this page
    https://hdl.handle.net/1911/19812
    Metadata
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    • DSP Publications [508]
    • ECE Publications [1468]

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    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
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    Site Map