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Visible and thermal infrared are two imaging modalities which are used in a variety of applications. In deep learning we need large datasets to be able to train and optimize the algorithms. In thermal infrared imaging, there is a lack of large datasets. This work proposes a deep learning approach to transform visible light images into thermal infrared images using video sequences with moving objects. We propose to use and optimize a CycleGAN algorithm to transform frames from one spectrum to another by training two generators and two discriminators. The results are promising with impressive qualitative and quantitative results.
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Marc-André Blais, Moulay A. Akhloufi, "Deep visible to thermal infrared style transfer in dynamic video sequences," Proc. SPIE 12109, Thermosense: Thermal Infrared Applications XLIV, 121090I (27 May 2022); https://doi.org/10.1117/12.2621608