Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion

Authors:
Jianwei Ma;G. Plonka
Affiliations:
Univ. Joseph Fourier, Grenoble;-
Venue:
IEEE Transactions on Image Processing
Year:
2007

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Abstract

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.