Share Email Print

Journal of Electronic Imaging

Wavelet-based image denoising using contextual hidden Markov tree model
Author(s): Din-Chang Tseng; Ming-Yu Shih
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The hidden Markov tree (HMT) model is a novel statistical model for image processing on wavelet domain. The HMT model captures the persistence property of wavelet coefficients, but lacks the clustering property of wavelet coefficients within a scale. We propose the contextual hidden Markov tree (CHMT) model to enhance the clustering property of the HMT model by adding extended coefficients associated with wavelet coefficients. The extended coefficients do not change the wavelet tree structure but enhance the intrascale dependencies of the HMT model. Hence, the training scheme of the HMT model can be modified to estimate the parameters of the CHMT model. In experiments, the proposed CHMT model produces almost better results than the HMT model produces for image denoising. Furthermore, the CHMT model requires fewer iterations of training than the HMT model to achieve the same denoised results.

Paper Details

Date Published: 1 July 2005
PDF: 12 pages
J. Electron. Imag. 14(3) 033005 doi: 10.1117/1.1994875
Published in: Journal of Electronic Imaging Volume 14, Issue 3
Show Author Affiliations
Din-Chang Tseng, National Central Univ. (Taiwan)
Ming-Yu Shih, National Central Univ. (Taiwan)

© SPIE. Terms of Use
Back to Top