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

Zhu, Minchen; Alperovich, Anna; Johannsen, Ole; Sulc, Antonin; Goldlücke, Bastian

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

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2019

Autor:innen

Zhu, Minchen

DOI (zitierfähiger Link)

https://doi.org/10.1109/CVPRW.2019.00236

Angaben zur Forschungsförderung

European Union (EU): 336978

Projekt

LIA - Light Field Imaging and Analysis

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen 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

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.

Fachgebiet (DDC)

004 Informatik

Konferenz

CVPRW 2019, 16. Juni 2019 - 20. Juni 2019, Long Beach, California

Zitieren

ISO 690

ZHU, 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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Universitätsbibliographie

Ja