On Covariant Derivatives and Their Applications to Image Regularization

TitleOn Covariant Derivatives and Their Applications to Image Regularization
Publication TypeJournal Article
Year of Publication2014
AuthorsBatard T, Bertalmío M
JournalSIAM Journal on Imaging Sciences (SIIMS)
Abstract

We present a generalization of the Euclidean and Riemannian gradient operators to a vector bundle, a geometric structure generalizing the concept of manifold. One of the key ideas is to replace the standard differentiation of a function by the covariant differentiation of a section. Dealing with covariant derivatives satisfying the property of compatibility with vector bundle metrics, we construct generalizations of existing mathematical models for image regularization that involve the Euclidean gradient operator, namely the linear scale-space and the Rudin-Osher-Fatemi denoising model. For well-chosen covariant derivatives, we show that our denoising model outperforms state-of-the-art variational denoising methods of the same type both in terms of PSNR and Q-index [45].

DOI10.1137/140954039