Bayesian Wavelet Domain Image Modeling using Hidden Markov Trees
| dc.citation.bibtexName | inproceedings | en_US |
| dc.citation.conferenceName | IEEE International Conference on Image Processing | en_US |
| dc.citation.firstpage | 158 | |
| dc.citation.lastpage | 162 | |
| dc.citation.location | Kobe, Japan | en_US |
| dc.citation.volumeNumber | 1 | en_US |
| dc.contributor.author | Romberg, Justin | en_US |
| dc.contributor.author | Choi, Hyeokho | en_US |
| dc.contributor.author | Baraniuk, Richard G. | en_US |
| dc.contributor.org | Digital Signal Processing (http://dsp.rice.edu/) | en_US |
| dc.date.accessioned | 2007-10-31T01:02:18Z | |
| dc.date.available | 2007-10-31T01:02:18Z | |
| dc.date.issued | 1999-10-01 | en |
| dc.date.modified | 2006-06-21 | en_US |
| dc.date.note | 2001-10-10 | en_US |
| dc.date.submitted | 1999-10-01 | en_US |
| dc.description | Conference paper | en_US |
| dc.description.abstract | Wavelet-domain hidden Markov models have proven to be useful tools for statiscal signal and image processing. The hidden Markov tree (HMT) model captures the key features o teh join 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 teh iHMT to just nine easily trained parameters (independent of the size of teh 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.citation | J. Romberg, H. Choi and R. G. Baraniuk, "Bayesian Wavelet Domain Image Modeling using Hidden Markov Trees," vol. 1, 1999. | |
| dc.identifier.doi | http://dx.doi.org/10.1109/ICIP.1999.821586 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/20290 | |
| dc.language.iso | eng | |
| dc.subject | bayesian | * |
| dc.subject | wavelet | * |
| dc.subject | image modeling | * |
| dc.subject | hidden markov trees | * |
| dc.subject.keyword | bayesian | en_US |
| dc.subject.keyword | wavelet | en_US |
| dc.subject.keyword | image modeling | en_US |
| dc.subject.keyword | hidden markov trees | en_US |
| dc.subject.other | Wavelet based Signal/Image Processing | en_US |
| dc.title | Bayesian Wavelet Domain Image Modeling using Hidden Markov Trees | en_US |
| dc.type | Conference paper | |
| dc.type.dcmi | Text |