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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, Multi-view, Multi-frame, Super-Resolution, Deep Learning, Attention Block.

Abstract: This paper proposes a novel CNN architecture for the multi-thermal image super-resolution problem. In the proposed scheme, the multi-images are synthetically generated by downsampling and slightly shifting the given image; noise is also added to each of these synthesized images. The proposed architecture uses two attention blocks paths to extract high-frequency details taking advantage of the large information extracted from multiple images of the same scene. Experimental results are provided, showing the proposed scheme has overcome the state-of-the-art approaches.

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Paper citation in several formats:
Rivadeneira, R.; Sappa, A. and Vintimilla, B. (2022). Multi-Image Super-Resolution for Thermal Images. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 635-642. DOI: 10.5220/0010899500003124

@conference{visapp22,
author={Rafael E. Rivadeneira. and Angel D. Sappa. and Boris X. Vintimilla.},
title={Multi-Image Super-Resolution for Thermal Images},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={635-642},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010899500003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Multi-Image Super-Resolution for Thermal Images
SN - 978-989-758-555-5
IS - 2184-4321
AU - Rivadeneira, R.
AU - Sappa, A.
AU - Vintimilla, B.
PY - 2022
SP - 635
EP - 642
DO - 10.5220/0010899500003124
PB - SciTePress

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