@inproceedings{ed61a0f2c0e8466792deace50f20cbab,
title = "Unpaired thermal to visible spectrum transfer using adversarial training",
abstract = "Thermal Infrared (TIR) cameras are gaining popularity in many computer vision applications due to their ability to operate under low-light conditions. Images produced by TIR cameras are usually difficult for humans to perceive visually, which limits their usability. Several methods in the literature were proposed to address this problem by transforming TIR images into realistic visible spectrum (VIS) images. However, existing TIR-VIS datasets suffer from imperfect alignment between TIR-VIS image pairs which degrades the performance of supervised methods. We tackle this problem by learning this transformation using an unsupervised Generative Adversarial Network (GAN) which trains on unpaired TIR and VIS images. When trained and evaluated on KAIST-MS dataset, our proposed methods was shown to produce significantly more realistic and sharp VIS images than the existing state-of-the-art supervised methods. In addition, our proposed method was shown to generalize very well when evaluated on a new dataset of new environments.",
keywords = "Colorization, Generative Adversarial Networks, Thermal imaging, Unsupervised learning",
author = "Adam Nyberg and Abdelrahman Eldesokey and David Bergstr{\"o}m and David Gustafsson",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 15th European Conference on Computer Vision, ECCV 2018 ; Conference date: 08-09-2018 Through 14-09-2018",
year = "2019",
doi = "10.1007/978-3-030-11024-6_49",
language = "English (US)",
isbn = "9783030110239",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "657--669",
editor = "Laura Leal-Taix{\'e} and Stefan Roth",
booktitle = "Computer Vision – ECCV 2018 Workshops, Proceedings",
address = "Germany",
}