@Article{RafaelE.Rivadeneira2022,
author="Rafael E. Rivadeneira
and Angel Sappa
and Boris X. Vintimilla
and Riad I. Hammoud",
title="A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution",
journal="Sensors",
year="2022",
volume="22",
number="6",
pages="2254",
optkeywords="Thermal image super-resolution",
optkeywords="unsupervised super-resolution",
optkeywords="thermal images",
optkeywords="attention module",
optkeywords="semiregistered thermal images",
abstract="This paper presents a transfer domain strategy to tackle the limitations of low-resolution thermal sensors and generate higher-resolution images of reasonable quality. The proposed technique employs a CycleGAN architecture and uses a ResNet as an encoder in the generator along with an attention module and a novel loss function. The network is trained on a multi-resolution thermal image dataset acquired with three different thermal sensors. Results report better performance benchmarking results on the 2nd CVPR-PBVS-2021 thermal image super-resolution challenge than state-of-the-art methods. The code of this work is available online.",
optnote="MSIAU;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3688), last updated on Tue, 25 Apr 2023 15:33:39 +0200",
doi="10.3390/s22062254",
file=":http://refbase.cvc.uab.es/files/RSV2022b.pdf:PDF"
}