Combined curvelet shrinkage and nonlinear anisotropic diffusion.
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.
Ma
Jianwei
J
Laboratoire LMC-IMAG, University Joseph Fourier, 38041 Grenoble Cedex 9, France. jma@tsinghua.edu.cn
Plonka
Gerlind
G
D000465:Algorithms; D016880:Anisotropy; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017711:Nonlinear Dynamics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity