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|a Rivadeneira, Rafael E.
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|a Sappa, Angel D.
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|a Vintimilla, Boris X.
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|a Hammoud, Riad
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|a A Novel Domain Transfer-Based Approach for Unsupervised Thermal Image Super-Resolution
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|b Multidisciplinary Digital Publishing Institute,
|c 2022-03-24T19:01:58Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/141368
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|a 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.
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