Total Variation Regularization for Manifold-Valued Data
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 $\ell pi $-type data terms in the manifold case. These algorithms are based on iterative geodesic averaging whi...
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Veröffentlicht in: | SIAM journal on imaging sciences 2014-01, Vol.7 (4), p.2226-2257 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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 $\ell pi $-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. |
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ISSN: | 1936-4954 1936-4954 |
DOI: | 10.1137/130951075 |