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dc.contributor.authorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G.
Kingsbury, Nicholas G.
dc.creatorRomberg, Justin
Choi, Hyeokho
Baraniuk, Richard G.
Kingsbury, Nicholas G.
dc.date.accessioned 2007-10-31T01:02:46Z
dc.date.available 2007-10-31T01:02:46Z
dc.date.issued 2002-05-01
dc.date.submitted 2002-07-10
dc.identifier.urihttps://hdl.handle.net/1911/20299
dc.description Journal Paper
dc.description.abstract Multiresolution models such as the hidden Markov tree (HMT) aim to capture the statistical structure of signals and images by leveraging two key wavelet transform properties: wavelet coefficients representing smooth/singular regions in a signal have small/large magnitude, and small/large magnitudes persist through scale. Unfortunately, the HMT based on the conventional (fully decimated) wavelet transform suffers from shift-variance, making it less accurate and realistic. In this paper, we extend the HMT modeling framework to the complex wavelet transform, which features near shift-invariance and improved directionality compared to the standard wavelet transform. The complex HMT model is computationally efficient (with linear-time computation and processing algorithms) and applicable to general Bayesian inference problems as a prior density for images. We demonstrate the effectiveness of the model with two applications. In a simple estimation experiment, the complex wavelet HMT model outperforms a number of high-performance denoising algorithms, including redundant wavelet thresholding (cycle spinning) and the redundant HMT. A multiscale maximum likelihood texture classification algorithm produces fewer errors with the new model than with a standard HMT.
dc.language.iso eng
dc.subjectcomplex wavelets
hidden markov models
image denoising
dc.subject.otherImage Processing and Pattern analysis
Wavelet based Signal/Image Processing
dc.title Hidden Markov Tree Models for Complex Wavelet Transforms
dc.type Journal article
dc.citation.bibtexName article
dc.citation.journalTitle IEEE Transactions on Signal Processing
dc.date.modified 2006-06-26
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordcomplex wavelets
hidden markov models
image denoising
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.citation J. Romberg, H. Choi, R. G. Baraniuk and N. G. Kingsbury, "Hidden Markov Tree Models for Complex Wavelet Transforms," IEEE Transactions on Signal Processing, 2002.


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1289]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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