A Class of Generalized Laplacians on Vector Bundles Devoted to Multi-Channel Image Processing

Authors:
Thomas Batard;Nir Sochen
Affiliations:
Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel 69978;Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel 69978
Venue:
Journal of Mathematical Imaging and Vision
Year:
2014

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Abstract

In the context of fibre bundles theory, there exist some differential operators of order 2, called generalized Laplacians, acting on sections of vector bundles over Riemannian manifolds, and generalizing the Laplace-Beltrami operator. Such operators are determined by covariant derivatives on vector bundles. In this paper, we construct a class of generalized Laplacians, devoted to multi-channel image processing, from the construction of optimal covariant derivatives. The key idea is to consider an image as a section of an associate bundle, that is a vector bundle related to a principal bundle through a group representation. In this context, covariant derivatives are determined by connection 1-forms on principal bundles. We construct optimal connection 1-forms by the minimization of a variational problem on principal bundles. From the heat equations of the generalized Laplacians induced by the corresponding optimal covariant derivatives, we obtain diffusions whose behaviors depend of the choice of the group representation. We provide experiments on color images.