Hidden Markov Tree Models for Complex Wavelet Transforms
| dc.citation.bibtexName | article | en_US |
| dc.citation.journalTitle | IEEE Transactions on Signal Processing | 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.author | Kingsbury, Nicholas G. | en_US |
| dc.contributor.org | Center for Multimedia Communications (http://cmc.rice.edu/) | en_US |
| dc.contributor.org | Digital Signal Processing (http://dsp.rice.edu/) | en_US |
| dc.date.accessioned | 2007-10-31T01:02:46Z | |
| dc.date.available | 2007-10-31T01:02:46Z | |
| dc.date.issued | 2002-05-01 | en |
| dc.date.modified | 2006-06-26 | en_US |
| dc.date.submitted | 2002-07-10 | en_US |
| dc.description | Journal Paper | en_US |
| 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. | en_US |
| dc.identifier.citation | J. Romberg, H. Choi, R. G. Baraniuk and N. G. Kingsbury, "Hidden Markov Tree Models for Complex Wavelet Transforms," <i>IEEE Transactions on Signal Processing,</i> 2002. | |
| dc.identifier.uri | https://hdl.handle.net/1911/20299 | |
| dc.language.iso | eng | |
| dc.subject | complex wavelets | * |
| dc.subject | hidden markov models | * |
| dc.subject | image denoising | * |
| dc.subject.keyword | complex wavelets | en_US |
| dc.subject.keyword | hidden markov models | en_US |
| dc.subject.keyword | image denoising | en_US |
| dc.subject.other | Image Processing and Pattern analysis | en_US |
| dc.subject.other | Wavelet based Signal/Image Processing | en_US |
| dc.title | Hidden Markov Tree Models for Complex Wavelet Transforms | en_US |
| dc.type | Journal article | |
| dc.type.dcmi | Text |