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Journal of Electronic Imaging

Context adaptive image denoising through modeling of curvelet domain statistics
Author(s): Linda Tessens; Aleksandra Pizurica; Alin Alecu; Adrian Munteanu; Wilfried R. Philips
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Paper Abstract

We perform a statistical analysis of curvelet coefficients, distinguishing between two classes of coefficients: those that contain a significant noise-free component, which we call the “signal of interest,” and those that do not. By investigating the marginal statistics, we develop a prior model for curvelet coefficients. The analysis of the joint intra- and inter-band statistics enables us to develop an appropriate local spatial activity indicator for curvelets. Finally, based on our findings, we present a novel denoising method, inspired by a recent wavelet domain method called ProbShrink. The new method outperforms its wavelet-based counterpart and produces results that are close to those of state-of-the-art denoisers.

Paper Details

Date Published: 1 July 2008
PDF: 17 pages
J. Electron. Imag. 17(3) 033021 doi: 10.1117/1.2987723
Published in: Journal of Electronic Imaging Volume 17, Issue 3
Show Author Affiliations
Linda Tessens, Univ. Gent (Belgium)
Aleksandra Pizurica, Univ. Gent (Belgium)
Alin Alecu, Vrije Univ. Brussel (Belgium)
Adrian Munteanu, Vrije Univ. Brussel (Belgium)
Wilfried R. Philips, Univ. Gent (Belgium)

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