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, Linda Tessens, Aleksandra Pizurica, Aleksandra Pizurica, Alin Alecu, Alin Alecu, Adrian Munteanu, Adrian Munteanu, Wilfried R. Philips, Wilfried R. Philips, } "Context adaptive image denoising through modeling of curvelet domain statistics," Journal of Electronic Imaging 17(3), 033021 (1 July 2008). https://doi.org/10.1117/1.2987723 . Submission:

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