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1 July 2008 Context adaptive image denoising through modeling of curvelet domain statistics
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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.
©(2008) Society of Photo-Optical Instrumentation Engineers (SPIE)
Linda Tessens, Aleksandra Pizurica, Alin Alecu, Adrian Munteanu, and Wilfried R. Philips "Context adaptive image denoising through modeling of curvelet domain statistics," Journal of Electronic Imaging 17(3), 033021 (1 July 2008).
Published: 1 July 2008

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