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Authors: Rafael E. Rivadeneira 1 ; Angel D. Sappa 2 ; 1 and Boris X. Vintimilla 1

Affiliations: 1 Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería en Electricidad y Computación, CIDIS, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador ; 2 Computer Vision Center, Edifici O, Campus UAB, 08193 Bellaterra, Barcelona, Spain

Keyword(s): Thermal Images, Far Infrared, Dataset, Super-resolution.

Abstract: This paper proposes a novel CycleGAN architecture for thermal image super-resolution, together with a large dataset consisting of thermal images at different resolutions. The dataset has been acquired using three thermal cameras at different resolutions, which acquire images from the same scenario at the same time. The thermal cameras are mounted in a rig trying to minimize the baseline distance to make easier the registration problem. The proposed architecture is based on ResNet6 as a Generator and PatchGAN as a Discriminator. The novelty on the proposed unsupervised super-resolution training (CycleGAN) is possible due to the existence of aforementioned thermal images—images of the same scenario with different resolutions. The proposed approach is evaluated in the dataset and compared with classical bicubic interpolation. The dataset and the network are available.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Rivadeneira, R.; Sappa, A. and Vintimilla, B. (2020). Thermal Image Super-resolution: A Novel Architecture and Dataset. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 111-119. DOI: 10.5220/0009173601110119

@conference{visapp20,
author={Rafael E. Rivadeneira. and Angel D. Sappa. and Boris X. Vintimilla.},
title={Thermal Image Super-resolution: A Novel Architecture and Dataset},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={111-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009173601110119},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Thermal Image Super-resolution: A Novel Architecture and Dataset
SN - 978-989-758-402-2
IS - 2184-4321
AU - Rivadeneira, R.
AU - Sappa, A.
AU - Vintimilla, B.
PY - 2020
SP - 111
EP - 119
DO - 10.5220/0009173601110119
PB - SciTePress

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