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German Conference on Pattern Recognition

GCPR 2017: Pattern Recognition pp 90–100Cite as

Trainable Regularization for Multi-frame Superresolution

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10496))

Abstract

In this paper, we present a novel method for multi-frame superresolution (SR). Our main goal is to improve the spatial resolution of a multi-line scan camera for an industrial inspection task. High resolution output images are reconstructed using our proposed SR algorithm for multi-channel data, which is based on the trainable reaction-diffusion model. As this is a supervised learning approach, we simulate ground truth data for a real imaging scenario. We show that learning a regularizer for the SR problem improves the reconstruction results compared to an iterative reconstruction algorithm using TV or TGV regularization. We test the learned regularizer, trained on simulated data, on images acquired with the real camera setup and achieve excellent results.

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Acknowledgements

We acknowledge grant support from the FWF START project BIVISION, No. Y729, the ERC starting grant HOMOVIS, No. 640156 and from the AIT and the Austrian Federal Ministry of Science under the HRSM programme BGBl. II Nr. 292/2012.

Author information

Authors and Affiliations

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

    Teresa Klatzer, Erich Kobler, Kerstin Hammernik & Thomas Pock

  2. Center for Vision, Automation and Control, AIT, Vienna, Austria

    Daniel Soukup & Thomas Pock

Authors
  1. Teresa Klatzer
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  2. Daniel Soukup
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  3. Erich Kobler
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  4. Kerstin Hammernik
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  5. Thomas Pock
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Corresponding author

Correspondence to Teresa Klatzer .

Editor information

Editors and Affiliations

  1. University of Basel, Basel, Switzerland

    Volker Roth

  2. University of Basel, Basel, Switzerland

    Thomas Vetter

1 Electronic supplementary material

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Supplementary material 1 (pdf 205 KB)

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Klatzer, T., Soukup, D., Kobler, E., Hammernik, K., Pock, T. (2017). Trainable Regularization for Multi-frame Superresolution. In: Roth, V., Vetter, T. (eds) Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science(), vol 10496. Springer, Cham. https://doi.org/10.1007/978-3-319-66709-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-66709-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66708-9

  • Online ISBN: 978-3-319-66709-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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