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Authors: Vida Fakour-Sevom 1 ; Esin Guldogan 2 and Joni-Kristian Kämäräinen 3

Affiliations: 1 Nokia Technologies and Tampere University of Technology, Finland ; 2 Nokia Technologies, Finland ; 3 Tampere University of Technology, Finland

Keyword(s): Super-resolution, Virtual Reality, Equirectangular Panorama, Deep Convolutional Neural Network.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image Enhancement and Restoration ; Image Formation and Preprocessing

Abstract: We propose deep convolutional neural network (CNN) based super-resolution for 360 (equirectangular) panorama images used by virtual reality (VR) display devices (e.g. VR glasses). Proposed super-resolution adopts the recent CNN architecture proposed in (Dong et al., 2016) and adapts it for equirectangular panorama images which have specific characteristics as compared to standard cameras (e.g. projection distortions). We demonstrate how adaptation can be performed by optimizing the trained network input size and fine-tuning the network parameters. In our experiments with 360 panorama images of rich natural content CNN based super-resolution achieves average PSNR improvement of 1.36 dB over the baseline (bicubic interpolation) and 1.56 dB by our equirectangular specific adaptation.

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Paper citation in several formats:
Fakour-Sevom, V.; Guldogan, E. and Kämäräinen, J. (2018). 360 Panorama Super-resolution using Deep Convolutional Networks. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 159-165. DOI: 10.5220/0006618901590165

@conference{visapp18,
author={Vida Fakour{-}Sevom. and Esin Guldogan. and Joni{-}Kristian Kämäräinen.},
title={360 Panorama Super-resolution using Deep Convolutional Networks},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={159-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006618901590165},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - 360 Panorama Super-resolution using Deep Convolutional Networks
SN - 978-989-758-290-5
IS - 2184-4321
AU - Fakour-Sevom, V.
AU - Guldogan, E.
AU - Kämäräinen, J.
PY - 2018
SP - 159
EP - 165
DO - 10.5220/0006618901590165
PB - SciTePress

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