Context adaptive image denoising through modeling of curvelet domain statistics

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.

• Type: Journal
• Year: 2008
• Where: JEI
• Authors: Linda Tessens, Aleksandra Pizurica, Alin Alecu, Adrian Munteanu, Wilfried Philips
• Keywords: Curvelet Coefficients, JEI 2008, Significant Noise-free Component, Spatial Activity Indicator