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A Convex Approach for Variational Super-Resolution

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Pattern Recognition (DAGM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6376))

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Abstract

We propose a convex variational framework to compute high resolution images from a low resolution video. The image formation process is analyzed to provide to a well designed model for warping, blurring, downsampling and regularization. We provide a comprehensive investigation of the single model components. The super-resolution problem is modeled as a minimization problem in an unified convex framework, which is solved by a fast primal dual algorithm. A comprehensive evaluation on the influence of different kinds of noise is carried out. The proposed algorithm shows excellent recovery of information for various real and synthetic datasets.

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Author information

Authors and Affiliations

  1. Institute for Computer Graphics and Vision, Graz University of Technology, Austria

    Markus Unger, Thomas Pock, Manuel Werlberger & Horst Bischof

Authors
  1. Markus Unger
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  2. Thomas Pock
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  3. Manuel Werlberger
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  4. Horst Bischof
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Editor information

Editors and Affiliations

  1. GRIS, TU Darmstadt, Fraunhoferstr. 5, 64283, Darmstadt, Germany

    Michael Goesele  & Stefan Roth  & 

  2. Fraunhofer Institute for Computer Graphics Research (IGD), Fraunhoferstr. 5, 64283, Darmstadt, Germany

    Arjan Kuijper

  3. Dept. of Computer Science, TU Darmstadt, Hochschulstrasse 10, 64289, Darmstadt, Germany

    Bernt Schiele

  4. TU Darmstadt, Hochschulstr. 10, 64289, Darmstadt, Germany

    Konrad Schindler

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© 2010 Springer-Verlag Berlin Heidelberg

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Unger, M., Pock, T., Werlberger, M., Bischof, H. (2010). A Convex Approach for Variational Super-Resolution. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_32

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  • DOI: https://doi.org/10.1007/978-3-642-15986-2_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15985-5

  • Online ISBN: 978-3-642-15986-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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