Nonlinear curvelet diffusion for noisy image enhancement

Digital image degradation normally arises during image acquisition and processing, which has a direct influence on the visual quality of the image. This paper proposes a combined method for enhancement of noisy image by using the mirror-extended curvelet transform and nonlinear anisotropic diffusion. First, an improved enhancement function is proposed to nonlinearly shrink and stretch the curvelet coefficients. Then, the enhanced results are further processed by the nonlinear diffusion where only the nonsignificant, i.e., nonthresholded, curvelet coefficients are changed by means of a diffusion process in order to reduce the pseudo-Gibbs artifacts. Experimental results indicate the proposed method has better performances to enhance the shape of edges and important detailed features as well as suppress noise, in comparison to the curvelet-based enhancement method without diffusion and the wavelet-based enhancement methods with/without diffusion.