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Total roto-translational variation

  • Antonin ChambolleEmail author
  • Thomas Pock
Article
  • 20 Downloads

Abstract

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.

Mathematics Subject Classification

53A04 49Q20 26A45 35J35 53A40 65K10 

Notes

Acknowledgements

The authors would like to thank Prof. R. Duits (T.U. Eindhoven), as well as the referees for their accurate reading of the paper and their helpful and positive comments. They also wish to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme “Variational methods, new optimisation techniques and new fast numerical algorithms” (Sept.–Oct., 2017), when this paper was completed. This work was supported by: EPSRC Grant No. EP/K032208/1. The work of A.C. was also partially supported by a Grant of the Simons Foundation. T.P. acknowledges support by the Austrian science fund (FWF) under the project EANOI, No. I1148 and the ERC starting Grant HOMOVIS, No. 640156.

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Authors and Affiliations

  1. 1.CMAP, Ecole PolytechniqueCNRSPalaiseauFrance
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria
  3. 3.Center for Vision, Automation and ControlAIT Austrian Institute of Technology GmbHViennaAustria

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