Non-smooth Variational Regularization for Processing Manifold-Valued Data

Martin Holler () and Andreas Weinmann ()
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Martin Holler: University of Graz, Institute of Mathematics and Scientific Computing
Andreas Weinmann: Hochschule Darmstadt, Department of Mathematics and Natural Sciences

Chapter Chapter 2 in Handbook of Variational Methods for Nonlinear Geometric Data, 2020, pp 51-93 from Springer

Abstract: Abstract Many methods for processing scalar and vector valued images, volumes and other data in the context of inverse problems are based on variational formulations. Such formulations require appropriate regularization functionals that model expected properties of the object to reconstruct. Prominent examples of regularization functionals in a vector-space context are the total variation (TV) and the Mumford-Shah functional, as well as higher-order schemes such as total generalized variation models. Driven by applications where the signals or data live in nonlinear manifolds, there has been quite some interest in developing analogous methods for nonlinear, manifold-valued data recently. In this chapter, we consider various variational regularization methods for manifold-valued data. In particular, we consider TV minimization as well as higher order models such as total generalized variation (TGV). Also, we discuss (discrete) Mumford-Shah models and related methods for piecewise constant data. We develop discrete energies for denoising and report on algorithmic approaches to minimize them. Further, we also deal with the extension of such methods to incorporate indirect measurement terms, thus addressing the inverse problem setup. Finally, we discuss wavelet sparse regularization for manifold-valued data.

Date: 2020
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DOI: 10.1007/978-3-030-31351-7_2

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