Oriented Half Gaussian Kernels and Anisotropic Diffusion

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.