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    Wavelet-Based Denoising Using Hidden Markov Models

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    Author
    Borran, Mohammad Jaber; Nowak, Robert David
    Date
    2001-09-17
    Abstract
    Hidden Markov models have been used in a wide variety of wavelet-based statistical signal processing applications. Typically, Gaussian mixture distributions are used to model the wavelet coefficients and the correlation between the magnitudes of the wavelet coefficients within each scale and/or across the scales is captured by a Markov tree imposed on the (hidden) states of the mixture. This paper investigates correlations directly among the wavelet coefficient amplitudes (sign à magnitude), instead of magnitudes alone. Our theoretical analysis shows that the coefficients display significant correlations in sign as well as magnitude, especially near strong edges. We propose a new wavelet-based HMM structure based on mixtures of one-sided exponential densities that exploits both sign and magnitude correlations. We also investigate the application of this for denoising the signals corrupted by additive white Gaussian noise. Using some examples with standard test signals, we show that our new method can achieve better mean squared error, and the resulting denoised signals are generally much smoother.
    Description
    Conference Paper
    Citation
    M. J. Borran and R. D. Nowak, "Wavelet-Based Denoising Using Hidden Markov Models," vol. 6, 2001.
    Published Version
    http://dx.doi.org/10.1109/ICASSP.2001.940702
    Keyword
    hidden markov models; wavlet-based denoising; Gaussian; hidden markov models; wavlet-based denoising; More... Gaussian Less...
    Type
    Conference paper
    Citable link to this page
    https://hdl.handle.net/1911/19741
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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