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Functional Lifting for Variational Problems with Higher-Order Regularization

  • Benedikt Loewenhauser
  • Jan LellmannEmail author
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Variational approaches are an established paradigm in the field of image processing. The non-convexity of the functional can be addressed by functional lifting and convex relaxation techniques, which aim to solve a convex approximation of the original energy on a larger space. However, so far these approaches have been limited to first-order , gradient-based regularizers such as the total variation . In this work, we propose a way to extend functional lifting to a second-order regularizer derived from the Laplacian. We prove that it can be represented efficiently and thus allows numerical optimization. We experimentally demonstrate the usefulness on a synthetic convex denoising problem and on synthetic as well as real-world image registration problems.

Notes

Acknowledgements

This work was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—394737018 “Functional Lifting 2.0: Efficient Convexifications for Imaging and Vision”. We would like to thank Emanuel Laude and Thomas Möllenhoff for providing their library prost, which was used to solve the saddle-point formulation of the problems.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Mathematics and Image Computing (MIC)University of LübeckLübeckGermany
  2. 2.Technical University of MunichGarchingGermany
  3. 3.Institute of Mathematics and Image Computing (MIC)University of LübeckLübeckGermany

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