Bayesian Tree-Structured Image Modeling

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE Southwest Symposium on Image Analysis and Interpretationen_US
dc.citation.firstpage232
dc.citation.lastpage236
dc.citation.locationAustin, TXen_US
dc.contributor.authorRomberg, Justinen_US
dc.contributor.authorChoi, Hyeokhoen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:02:26Z
dc.date.available2007-10-31T01:02:26Z
dc.date.issued2000-04-01en
dc.date.modified2006-06-07en_US
dc.date.note2006-06-07en_US
dc.date.submitted2000-04-01en_US
dc.descriptionConference paperen_US
dc.description.abstractWavelet-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 statistics of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training (using the EM algorithm, for example). In this paper, we propose two reduced-parameter HMT models that capture the general structure of a broad class of grayscale images. The image HMT (iHMT) model leverages the fact that for a large class of images the structure of the HMT is self-similar across scale. This allows us to reduce the complexity of the iHMT to just nine easily trained parameters (independent of the size of the image and the number of wavelet scales). In the universal HMT (uHMT) we take a Bayesian approach and fix these nine parameters. The uHMT requires no training of any kind. While simple, we show using a series of image estimation/denoising experiments that these two new models retain nearly all of the key structures modeled by the full HMT. Based on these new models, we develop a shift-invariant wavelet denoising scheme that outperforms all algorithms in the current literature.en_US
dc.identifier.citationJ. Romberg, H. Choi and R. G. Baraniuk, "Bayesian Tree-Structured Image Modeling," 2000.
dc.identifier.urihttps://hdl.handle.net/1911/20293
dc.language.isoeng
dc.subject.otherDSP for Communicationsen_US
dc.titleBayesian Tree-Structured Image Modelingen_US
dc.typeConference paper
dc.type.dcmiText
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