In this paper, 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 "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 ProbShrink. The new method outperforms its wavelet-based counterpart and produces results that are close to those of state-of-the-art denoisers.
Tessens, L, Pizurica, A, Alecu, A , Munteanu, A & Philips, W 2008, ' Context adaptive image denoising through modeling of curvelet domain statistics ', Journal of Electronic Imaging , vol. 17, no. 3, 033021, pp. 1-17.
Tessens, L., Pizurica, A., Alecu, A. , Munteanu, A. , & Philips, W. (2008). Context adaptive image denoising through modeling of curvelet domain statistics . Journal of Electronic Imaging , 17 (3), 1-17. [033021].
@article{964c54ba82e54c4d88488ad2bb051542,
title = " Context adaptive image denoising through modeling of curvelet domain statistics " ,
abstract = " In this paper, 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 { " }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 statisticsenables 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 ProbShrink. The new method outperforms its wavelet-based counterpart and produces results that are close to those of state-of-the-art denoisers. " ,
keywords = " Curvelets, Image statistics, image denoising " ,
author = " Linda Tessens and Alexandra Pizurica and Alin Alecu and Adrian Munteanu and Wilfried Philips " ,
year = " 2008 " ,
month = sep,
language = " English " ,
volume = " 17 " ,
pages = " 117 " ,
journal = " Journal of Electronic Imaging " ,
issn = " 1017-9909 " ,
publisher = " SPIE " ,
number = " 3 " ,
}