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Context enhancement is critical for night vision (NV) applications,especially for the dark night situation without any artificial lights. In thispaper, we present the infrared-to-visual (IR2VI) algorithm, a novelunsupervised thermal-to-visible image translation framework based on generativeadversarial networks (GANs). IR2VI is able to learn the intrinsiccharacteristics from VI images and integrate them into IR images. Since theexisting unsupervised GAN-based image translation approaches face severalchallenges, such as incorrect mapping and lack of fine details, we propose astructure connection module and a region-of-interest (ROI) focal loss method toaddress the current limitations. Experimental results show the superiority ofthe IR2VI algorithm over baseline methods.

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation