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Total roto-translational variation. (English) Zbl 1418.53004

Summary: We consider curvature depending variational models for image regularization, such as Euler’s elastica. These models are known to provide strong priors for the continuity of edges and hence have important applications in shape- and image processing. We consider a lifted convex representation of these models in the roto-translation space: in this space, curvature depending variational energies are represented by means of a convex functional defined on divergence free vector fields. The line energies are then easily extended to any scalar function. It yields a natural generalization of the total variation to curvature-dependent energies. As our main result, we show that the proposed convex representation is tight for characteristic functions of smooth shapes. We also discuss cases where this representation fails. For numerical solution, we propose a staggered grid discretization based on an averaged Raviart-Thomas finite elements approximation. This discretization is consistent, up to minor details, with the underlying continuous model. The resulting non-smooth convex optimization problem is solved using a first-order primal-dual algorithm. We illustrate the results of our numerical algorithm on various problems from shape- and image processing.

MSC:

53A04 Curves in Euclidean and related spaces
49Q20 Variational problems in a geometric measure-theoretic setting
26A45 Functions of bounded variation, generalizations
35J35 Variational methods for higher-order elliptic equations
53A40 Other special differential geometries
65K10 Numerical optimization and variational techniques

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