3-D Directional Diffusion

In this paper we propose a 3-D filter for denoising and enhancing strongly oriented data. Our approach is based on 2-D directional diffusion model proposed in [1] [2]. The diffusion process is modulated by a non-linear function depending on the absolute values of the directional derivatives in the 3 orthogonal directions of the space. These directions are the eigenvectors of a structure tensor. We compare this extension with an existing 3-D extension designed by Dargent et al. [3].