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dc.contributor.authorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G.
dc.creatorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T01:02:33Z
dc.date.available 2007-10-31T01:02:33Z
dc.date.issued 1999-07-20
dc.date.submitted 1999-07-20
dc.identifier.urihttps://hdl.handle.net/1911/20295
dc.description Conference paper
dc.description.abstract Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training to fit an HMT model to a given data set (using the Expectation-Maximization algorithm, for example). In this paper, we greatly simplify the HMT model by exploiting the inherent self-similarity of real-world images. This simplified model specifies the HMT parameters with just nine meta-parameters (independent of the size of the image and the number of wavelet scales). We also introduce a Bayesian universal HMT (uHMT) that mixes these nine parameters. The uHMT requires no training of any kind. While extremely simple, we show using a series of image estimation/denoising experiments that these two new models retain nearly all of the key structure modeled by the full HMT. Finally, we propose a fast shift-invariant HMT estimation algorithm that outperforms other wavelet-based estimators in the current literature, both in mean-square error and visual metrics.
dc.description.sponsorship Texas Instruments
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjecthidden Markov tree (HMT)
wavelet
Bayesian universal
dc.subject.otherWavelet based Signal/Image Processing
dc.title Bayesian Tree-Structured Image Modeling using Wavelet-domain Hidden Markov Models
dc.type Conference paper
dc.date.note 2001-08-16
dc.citation.bibtexName inproceedings
dc.date.modified 2002-07-10
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordhidden Markov tree (HMT)
wavelet
Bayesian universal
dc.citation.location Denver, CO
dc.citation.conferenceName SPIE Conference on Mathematical Modeling, Bayesian Estimation, and Inverse Problem
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.citation J. Romberg, H. Choi and R. G. Baraniuk, "Bayesian Tree-Structured Image Modeling using Wavelet-domain Hidden Markov Models," 1999.


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1317]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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