NDLI: A complex wavelet domain Markov model for image denoising

Content Provider	IEEE Xplore Digital Library
Author	Zhen Ye Cheng-Chang Lu
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. Sci., Kent State Univ., OH, USA (Zhen Ye; Cheng-Chang Lu)
Abstract	Wavelet domain hidden Markov models (WHMMs) provide a powerful approach for image modeling and processing because of the clustering and persistence properties of wavelet coefficients. However, the shift-variance of real wavelet transforms degrades the accuracy of the WHMMs. To overcome this problem, we propose a hidden Markov model based on the dual-tree complex wavelet transform that is approximately shift-invariant. Context information is used in this model to indicate the local correlation among wavelet coefficients. According to different visual attributes, several contexts based on frequency, orientation and scale are applied to capture both intrascale and interscale dependencies. The parameters of this model are estimated by an EM algorithm. Applications to image denoising are presented. The denoising performance is among the best state-of-the-art techniques, and outperforms models, which are based on real discrete wavelet transforms (DWTs).
Sponsorship	IEEE Signal Process. Soc
File Size	340287
File Format	PDF
ISBN	0780377508
ISSN	15224880
DOI	10.1109/ICIP.2003.1247257
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-14
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet domain Image denoising Hidden Markov models Discrete wavelet transforms Wavelet coefficients Wavelet transforms Degradation Context modeling Frequency Noise reduction
Content Type	Text
Resource Type	Article

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