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12 October 2006 Spatially adaptive image denoising based on joint image statistics in the curvelet domain
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Proceedings Volume 6383, Wavelet Applications in Industrial Processing IV; 63830L (2006)
Event: Optics East 2006, 2006, Boston, Massachusetts, United States
In this paper, we perform a statistical analysis of curvelet coefficients, making a distinction between two classes of coefficients: those representing useful image content and those dominated by noise. By investigating the marginal statistics, we develop a mixture prior for curvelet coefficients. Through analysis of the joint intra-band statistics, we find that white Gaussian noise is transformed by the curvelet transform into noise that is correlated in one direction and decorrelated in the perpendicular direction. This enables us to develop an appropriate local spatial activity indicator for curvelets. Finally, based on our findings, we develop a novel denoising method, inspired by a recent wavelet domain method ProbShrink. For textured images, the new method outperforms its wavelet-based counterpart and existing curvelet-based methods. For piecewise smooth images, performances are similar as existing methods.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
L. Tessens, A. Pižurica, A. Alecu, A. Munteanu, and W. Philips "Spatially adaptive image denoising based on joint image statistics in the curvelet domain", Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830L (12 October 2006);


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