@InProceedings{RafaelE.Rivadeneira2022,
author="Rafael E. Rivadeneira
and Angel Sappa
and Boris X. Vintimilla",
title="Thermal Image Super-Resolution: A Novel Unsupervised Approach",
booktitle="International Joint Conference on Computer Vision, Imaging and Computer Graphics",
year="2022",
volume="1474",
pages="495--506",
abstract="This paper proposes the use of a CycleGAN architecture for thermal image super-resolution under a transfer domain strategy, where middle-resolution images from one camera are transferred to a higher resolution domain of another camera. The proposed approach is trained with a large dataset acquired using three thermal cameras at different resolutions. An unsupervised learning process is followed to train the architecture. Additional loss function is proposed trying to improve results from the state of the art approaches. Following the first thermal image super-resolution challenge (PBVS-CVPR2020) evaluations are performed. A comparison with previous works is presented showing the proposed approach reaches the best results.",
optnote="MSIAU; 600.130",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3776), last updated on Tue, 25 Apr 2023 14:13:04 +0200",
opturl="https://link.springer.com/chapter/10.1007/978-3-030-94893-1_23",
file=":http://refbase.cvc.uab.es/files/RSV2022d.pdf:PDF"
}