Authors:
Baptiste Magnier
and
Philippe Montesinos
Affiliation:
Ecole des Mines d’ALES, France
Keyword(s):
Half Anisotropic Gaussian Kernel, Diffusion PDEs.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
Abstract:
Nonlinear PDEs (partial differential equations) offer a convenient formal framework for image regularization
and are at the origin of several efficient algorithms. In this paper, we present a new approach which is based
(i) on a set of half Gaussian kernel filters, and (ii) a nonlinear anisotropic PDE diffusion. On one hand, half
Gaussian kernels provide oriented filters whose flexibility enables to detect edges with great accuracy. On the
other hand, a nonlinear anisotropic diffusion scheme offers a means to smooth images while preserving fine
structures or details, e.g. lines, corners and junctions. Based on the calculus of the gradient magnitude and
two diffusion directions, we construct a diffusion control function able to achieve precise image regularization.
Some quantified experimental results compared to existing PDEs approaches and a discussion about the
parameterizing of the method are presented.