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Context adaptive image denoising through modeling of curvelet domain statistics

[+] Author Affiliations
Linda Tessens

Ghent University, TELIN-IPI-IBBT, St-Pietersnieuwstraat 41, B-9000 Ghent, Belgium

Aleksandra Pižurica

Ghent University, TELIN-IPI-IBBT, St-Pietersnieuwstraat 41, B-9000 Ghent, Belgium

Alin Alecu

Vrije Universiteit Brussel, ETRO-IBBT, Pleinlaan 2, B-1050 Brussels, Belgium

Adrian Munteanu

Vrije Universiteit Brussel, ETRO-IBBT, Pleinlaan 2, B-1050 Brussels, Belgium

Wilfried Philips

Ghent University, TELIN-IPI-IBBT, St-Pietersnieuwstraat 41, B-9000 Ghent, Belgium

J. Electron. Imaging. 17(3), 033021 (September 29, 2008). doi:10.1117/1.2987723
History: Received September 14, 2007; Revised June 05, 2008; Accepted July 06, 2008; Published September 29, 2008
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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.

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© 2008 SPIE and IS&T

Citation

Linda Tessens ; Aleksandra Pižurica ; Alin Alecu ; Adrian Munteanu and Wilfried Philips
"Context adaptive image denoising through modeling of curvelet domain statistics", J. Electron. Imaging. 17(3), 033021 (September 29, 2008). ; http://dx.doi.org/10.1117/1.2987723


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