On Debiasing Restoration Algorithms: Applications to Total-Variation and Nonlocal-Means

  • Charles-Alban Deledalle
  • Nicolas Papadakis
  • Joseph Salmon
Conference paper

DOI: 10.1007/978-3-319-18461-6_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9087)
Cite this paper as:
Deledalle CA., Papadakis N., Salmon J. (2015) On Debiasing Restoration Algorithms: Applications to Total-Variation and Nonlocal-Means. 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

Bias in image restoration algorithms can hamper further analysis, typically when the intensities have a physical meaning of interest, e.g., in medical imaging. We propose to suppress a part of the bias – the method bias – while leaving unchanged the other unavoidable part – the model bias. Our debiasing technique can be used for any locally affine estimator including \(\ell _1\) regularization, anisotropic total-variation and some nonlocal filters.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Charles-Alban Deledalle
    • 1
    • 2
  • Nicolas Papadakis
    • 1
    • 2
  • Joseph Salmon
    • 3
  1. 1.IMBUniversity of BordeauxTalenceFrance
  2. 2.CNRS, IMBUniversity of BordeauxTalenceFrance
  3. 3.CNRS LTCIInstitut Mines-Télécom, Télécom ParisTechParisFrance

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