A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution
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sensors-22-02254.pdf
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Author(s)
Rivadeneira, Rafael E.
Sappa, Angel D.
Vintimilla, Boris X.
Hammoud, Riad
Date Issued
March 14, 2022
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Sensors 22 (6): 2254 (2022)
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/s22062254
