KEYWORDS: 3D modeling, Diffusion, Iron, Image processing, Anisotropic diffusion, Data modeling, Anisotropic filtering, Visualization, 3D image processing, Visual process modeling
We propose a 3D anisotropic diffusion filter for denoising and enhancing oriented data. The approach is a generalization
of a previous contribution and it is based on a novel, more precise orientation estimation step. The orientation in the 3D
space is computed using an asymmetric Isotropic Recursive Oriented Network (IRON) operator that can handle in a
natural way junctions and corners. In the experimental section we employ a set of 3D synthetic blocks to illustrate the
efficiency of the new method through quantitative and visual comparisons with other 3D-extended classical models or
recently proposed 3D Partial Differential Equations (PDE).
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