  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.
