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dc.contributor.authorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G.
dc.creatorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G. 2007-10-31T01:02:40Z 2007-10-31T01:02:40Z 2001-07-01 2002-07-10
dc.identifier.citation J. Romberg, H. Choi and R. G. Baraniuk, "Bayesian Tree-Structured Image Modeling using Wavelet-domain Hidden Markov Models," IEEE Transactions on Image Processing, vol. 10, no. 7, 2001.
dc.description Journal 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 fixes 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 new models retain nearly all of the key image 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 visually and in mean-square error.
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship Office of Naval Research
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjecthidden markov models
besov space
cycle spinning
image denoising
dc.subject.otherImage Processing and Pattern analysis
Wavelet based Signal/Image Processing
Multiscale Methods
dc.title Bayesian Tree-Structured Image Modeling using Wavelet-domain Hidden Markov Models
dc.type Journal article
dc.citation.bibtexName article
dc.citation.journalTitle IEEE Transactions on Image Processing 2006-06-06
dc.contributor.orgCenter for Multimedia Communications (
dc.contributor.orgDigital Signal Processing (
dc.subject.keywordhidden markov models
besov space
cycle spinning
image denoising
dc.citation.volumeNumber 10
dc.citation.issueNumber 7
dc.type.dcmi Text
dc.type.dcmi Text
dc.citation.firstpage 1056
dc.citation.lastpage 1068

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    Publications by Rice Faculty and graduate students in digital signal processing.
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    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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