Denoising is always a challenge problem in natural image and geophysical data processing. From the point of view of data assimilation, in this paper we consider the denoising of texture images in a scale space using a geometric wavelet based nonlinear reaction-diffusion equation, in which a curvelet shrinkage is used as a regularization of the diffusivity to preserve important features in the diffusion smoothing and a wave atom shrinkage is used as a pseudo-observation in the reaction for enhancement of interesting oriented textures. We named the general framework as image assimilation. The goal of the image assimilation is to link together these rich information such as sparse constraint in multiscale geometric space in order to retrieve the state of images. As a byproduct, we proposed a 2D wavelet-inspired numerical scheme for solving of the nonlinear diffusion. Experimental results show the performance of the proposed model for texture-preserving denoising and enhancement.

Date Published: 20 September 2007
PDF: 9 pages
Proc. SPIE 6701, Wavelets XII, 670123 (20 September 2007); doi: 10.1117/12.733054

Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)