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A Statistical Multiresolution Strategy for Image Reconstruction

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Scale Space and Variational Methods in Computer Vision (SSVM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6667))

Abstract

In this paper we present a fully data-driven and locally-adaptive method for image reconstruction that is based on the concept of statistical multiresolution estimation as introduced in [1]. It constitutes a statistical regularization technique that uses a ℓ ∞ -type distance measure as data fidelity combined with a convex cost functional. The resulting convex optimization problem is approached by a combination of an inexact augmented Lagrangian method and Dykstra’s projection algorithm.

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References

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Author information

Authors and Affiliations

  1. Institute for Mathematical Stochastics, University of Göttingen, Goldschmidtstrasse 7, 37077, Göttingen, Germany

    Klaus Frick & Philipp Marnitz

Authors
  1. Klaus Frick
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  2. Philipp Marnitz
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Editor information

Editors and Affiliations

  1. Computer Science Department, Technion — Israel Institute of Technology, 718 Taub Building Technion, 32000, Haifa, Israel

    Alfred M. Bruckstein

  2. Department of Biomedical Engineering, Biomedical Image Analysis and Interpretation, Eindhoven University of Technology, Den Dolech 2 – WH 2.101, 5600 MB, Eindhoven, The Netherlands

    Bart M. ter Haar Romeny

  3. Faculaty of Engineering, School of Electrical Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Alexander M. Bronstein

  4. Institute of Computationa Science, Faculty of Informatics SI-109, Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6904, Lugano, Switzerland

    Michael M. Bronstein

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Frick, K., Marnitz, P. (2012). A Statistical Multiresolution Strategy for Image Reconstruction. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_7

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  • DOI: https://doi.org/10.1007/978-3-642-24785-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24784-2

  • Online ISBN: 978-3-642-24785-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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