NDLI: Image denoising using bivariate α-stable distributions in the complex wavelet domain

Content Provider	IEEE Xplore Digital Library
Author	Achim, A. Kuruoglu, E.E.
Copyright Year	1994
Abstract	Recently, the dual-tree complex wavelet transform has been proposed as an analysis tool featuring near shift-invariance and improved directional selectivity compared to the standard wavelet transform. Within this framework, we describe a novel technique for removing noise from digital images. We design a bivariate maximum a posteriori estimator, which relies on the family of isotropic α-stable distributions. Using this relatively new statistical model we are able to better capture the heavy-tailed nature of the data as well as the interscale dependencies of wavelet coefficients. We test our algorithm for the Cauchy case, in comparison with several recently published methods. The simulation results show that our proposed technique achieves state-of-the-art performance in terms of root mean squared (RMS) error.
Sponsorship	IEEE Signal Processing Society
Starting Page	17
Ending Page	20
Page Count	4
File Size	545137
File Format	PDF
ISSN	10709908
Volume Number	12
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image denoising Wavelet domain Wavelet coefficients Signal processing algorithms Wavelet transforms Wavelet analysis Testing Bayesian methods Noise reduction Signal processing wavelet transform Alpha-stable distributions bivariate models MAP estimation Monte-Carlo methods
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing Electrical and Electronic Engineering

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