Wavelet-Based Denoising Using Hidden Markov Models

Files in this item

Files Size Format View
Bor2001May5Wavelet-Ba.PDF 161.9Kb application/pdf Thumbnail
Bor2001May5Wavelet-Ba.PPT 294.4Kb application/ View/Open

Show full item record

Item Metadata

Title: Wavelet-Based Denoising Using Hidden Markov Models
Author: Borran, Mohammad Jaber; Nowak, Robert David
Type: Conference paper
xmlui.Rice_ECE.Keywords: hidden markov models; wavlet-based denoising; Gaussian
Citation: M. J. Borran and R. D. Nowak, "Wavelet-Based Denoising Using Hidden Markov Models," vol. 6, pp. 3925-3928, 2001.
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.
Date Published: 2001-05-20

This item appears in the following Collection(s)

  • ECE Publications [1053 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.