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Total Variation Regularization for Manifold-Valued Data

A. Weinmann, L. Demaret, M. Storath

SIAM Journal on Imaging Sciences, vol. 7, no. 4, pp. 2226-2257, 2014.

We consider total variation (TV) minimization for manifold-valued data. We propose a cyclic proximal point algorithm and a parallel proximal point algorithm to minimize TV functionals with ℓp-type data terms in the manifold case. These algorithms are based on iterative geodesic averaging which makes them easily applicable to a large class of data manifolds. As an application, we consider denoising images which take their values in a manifold. We apply our algorithms to diffusion tensor images and interferometric SAR images as well as sphere- and cylinder-valued images. For the class of Cartan-Hadamard manifolds (which includes the data space in diffusion tensor imaging) we show the convergence of the proposed TV minimizing algorithms to a global minimizer.

AUTHOR="Weinmann, A. and Demaret, L. and Storath, M.",
TITLE="Total Variation Regularization for Manifold-Valued Data",
JOURNAL="{SIAM} Journal on Imaging Sciences",

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