An Epipolar Volume Autoencoder With Adversarial Loss for Deep Light Field Super-Resolution

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European Union (EU): 336978
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LIA - Light Field Imaging and Analysis
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2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition workshops : CVPRW 2019 : proceedings : 16-20 June 2019, Long Beach, California. Piscataway, NJ: IEEE, 2019, pp. 1853-1861. ISBN 978-1-72812-506-0. Available under: doi: 10.1109/CVPRW.2019.00236
Zusammenfassung

When capturing a light field of a scene, one typically faces a trade-off between more spatial or more angular resolution. Fortunately, light fields are also a rich source of information for solving the problem of super-resolution. Contrary to single image approaches, where high-frequency content has to be hallucinated to be the most likely source of the downscaled version, sub-aperture views from the light field can help with an actual reconstruction of those details that have been removed by downsampling. In this paper, we propose a three-dimensional generative adversarial autoencoder network to recover the high-resolution light field from a low-resolution light field with a sparse set of viewpoints. We require only three views along both horizontal and vertical axis to increase angular resolution by a factor of three while at the same time increasing spatial resolution by a factor of either two or four in each direction, respectively.

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CVPRW 2019, 16. Juni 2019 - 20. Juni 2019, Long Beach, California
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ISO 690ZHU, Minchen, Anna ALPEROVICH, Ole JOHANNSEN, Antonin SULC, Bastian GOLDLÜCKE, 2019. An Epipolar Volume Autoencoder With Adversarial Loss for Deep Light Field Super-Resolution. CVPRW 2019. Long Beach, California, 16. Juni 2019 - 20. Juni 2019. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition workshops : CVPRW 2019 : proceedings : 16-20 June 2019, Long Beach, California. Piscataway, NJ: IEEE, 2019, pp. 1853-1861. ISBN 978-1-72812-506-0. Available under: doi: 10.1109/CVPRW.2019.00236
BibTex
@inproceedings{Zhu2019-06Epipo-51260,
  year={2019},
  doi={10.1109/CVPRW.2019.00236},
  title={An Epipolar Volume Autoencoder With Adversarial Loss for Deep Light Field Super-Resolution},
  isbn={978-1-72812-506-0},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition workshops : CVPRW 2019 : proceedings : 16-20 June 2019, Long Beach, California},
  pages={1853--1861},
  author={Zhu, Minchen and Alperovich, Anna and Johannsen, Ole and Sulc, Antonin and Goldlücke, Bastian}
}
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