Hidden Markov Tree Modeling of Complex Wavelet Transforms

Simple item page
dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)en_US
dc.citation.firstpage133en_US
dc.citation.lastpage136en_US
dc.citation.locationIstanbul, Turkeyen_US
dc.citation.volumeNumber1en_US
dc.contributor.authorChoi, Hyeokhoen_US
dc.contributor.authorRomberg, Justinen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorKingsbury, Nicholas G.en_US
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:39:49Zen_US
dc.date.available2007-10-31T00:39:49Zen_US
dc.date.issued2000-06-01en_US
dc.date.modified2006-06-21en_US
dc.date.note2001-08-26en_US
dc.date.submitted2000-06-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractMultiresolution signal and image models such as the hidden Markov tree aim to capture the statistical structure of smooth and singular (edgy) regions. Unfortunately, models based on the orthogonal wavelet transform suffer from shift-variance, making them 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 angular resolution compared to the standard wavelet transform. The model is computationally efficient (with linear-time computation and processing algorithms) and applicable to general Bayesian inference problems as a prior density for the data. 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.en_US
dc.description.sponsorshipTexas Instrumentsen_US
dc.identifier.citationH. Choi, J. Romberg, R. G. Baraniuk and N. G. Kingsbury, "Hidden Markov Tree Modeling of Complex Wavelet Transforms," vol. 1, 2000.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/ICASSP.2000.861889en_US
dc.identifier.urihttps://hdl.handle.net/1911/19793en_US
dc.language.isoengen_US
dc.subjectmultiresolution signalen_US
dc.subjectimageen_US
dc.subjecthidden markov tree (HMT)en_US
dc.subjectmodelen_US
dc.subjectBayesianen_US
dc.subjectwavleten_US
dc.subject.keywordmultiresolution signalen_US
dc.subject.keywordimageen_US
dc.subject.keywordhidden markov tree (HMT)en_US
dc.subject.keywordmodelen_US
dc.subject.keywordBayesianen_US
dc.subject.keywordwavleten_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.titleHidden Markov Tree Modeling of Complex Wavelet Transformsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Cho2000Jun1HiddenMark.PDF
Size:
557.51 KB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
Cho2000Jun1HiddenMark.PS
Size:
927.51 KB
Format:
Postscript Files
Download
Collections
ECE Publications
DSP Publications