  Context enhancement is critical for night vision (NV) applications,
especially for the dark night situation without any artificial lights. In this
paper, we present the infrared-to-visual (IR2VI) algorithm, a novel
unsupervised thermal-to-visible image translation framework based on generative
adversarial networks (GANs). IR2VI is able to learn the intrinsic
characteristics from VI images and integrate them into IR images. Since the
existing unsupervised GAN-based image translation approaches face several
challenges, such as incorrect mapping and lack of fine details, we propose a
structure connection module and a region-of-interest (ROI) focal loss method to
address the current limitations. Experimental results show the superiority of
the IR2VI algorithm over baseline methods.
