Deep visible to thermal infrared style transfer in dynamic video sequences
Marc-André Blais, Moulay A. Akhloufi · 2022
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.