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dc.contributor.authorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G.
dc.creatorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T01:02:21Z
dc.date.available 2007-10-31T01:02:21Z
dc.date.issued 1999-10-20
dc.date.submitted 1999-10-20
dc.identifier.citation J. Romberg, H. Choi and R. G. Baraniuk, "Shift-Invariant Denoising using Wavelet-Domain Hidden Markov Trees," 1999.
dc.identifier.urihttps://hdl.handle.net/1911/20291
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 statistics of the wavelet coefficients of realworld 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 use image structure not yet recognized by the HMT to show that the HMT parameters of real-world, grayscale images have a certain form. This leads to a description of the HMT model with just nine meta-parameters (independent of the size of the image and the number of wavelet scales). We also observe that these nine meta-parameters are similar for many images. This leads to a universal HMT (uHMT) model for grayscale images. Algorithms using the uHMT require no training of any kind. While simple, a series of image estimation/denoising experiments show that the uHMT retains nearly all of the key structures modeled by the full HMT. Based on the uHMT model, we develop a shift-invariant wavelet denoising scheme that outperforms all algorithms in the current literature.
dc.language.iso eng
dc.subjectshift-invariant denoising
wavelet-domain hidden markov trees
dc.subject.otherWavelet based Signal/Image Processing
dc.title Shift-Invariant Denoising using Wavelet-Domain Hidden Markov Trees
dc.type Conference paper
dc.date.note 2001-10-10
dc.citation.bibtexName inproceedings
dc.date.modified 2002-07-10
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordshift-invariant denoising
wavelet-domain hidden markov trees
dc.citation.location Pacific Grove, CA
dc.citation.conferenceName Asilomar Conference on Signals, Systems, and Computers
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1109/ACSSC.1999.831912


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1278]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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