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Solution-Driven Adaptive Total Variation Regularization

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9087)

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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Correspondence to Frank Lenzen .

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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. https://doi.org/10.1007/978-3-319-18461-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-18461-6_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18460-9

  • Online ISBN: 978-3-319-18461-6

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