Solution-Driven Adaptive Total Variation Regularization

Conference paper

DOI: 10.1007/978-3-319-18461-6_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9087)
Cite this paper as:
Lenzen F., Berger J. (2015) Solution-Driven Adaptive Total Variation Regularization. In: Aujol JF., Nikolova M., Papadakis N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2015. Lecture Notes in Computer Science, vol 9087. Springer, Cham

Abstract

We consider solution-driven adaptive variants of Total Variation, in which the adaptivity is introduced as a fixed point problem. We provide existence theory for such fixed points in the continuous domain. For the applications of image denoising, deblurring and inpainting, we provide experiments which demonstrate that our approach in most cases outperforms state-of-the-art regularization approaches.

Keywords

Regularization Inverse problems Adaptive total variation Solution-driven adaptivity Fixed point problems Image restoration 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.HCI & IPA, University of HeidelbergHeidelbergGermany

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