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Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion

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2 Author(s)

In this paper, a diffusion-based curvelet shrinkage is proposed for discontinuity-preserving denoising using a combination of a new tight frame of curvelets with a nonlinear diffusion scheme. In order to suppress the pseudo-Gibbs and curvelet-like artifacts, the conventional shrinkage results are further processed by a projected total variation diffusion, in which only the insignificant curvelet coefficients or high-frequency part of the signal are changed by use of a constrained projection. Numerical experiments from piecewise-smooth to textured images show good performances of the proposed method to recover the shape of edges and important detailed components, in comparison to some existing methods.

Published in:

Image Processing, IEEE Transactions on  (Volume:16 ,  Issue: 9 )

Date of Publication:

Sept. 2007

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