  Over the past few years deep learning-based techniques such as Generative
Adversarial Networks (GANs) have significantly improved solutions to image
super-resolution and image-to-image translation problems. In this paper, we
propose a solution to the joint problem of image super-resolution and
multi-modality image-to-image translation. The problem can be stated as the
recovery of a high-resolution image in a modality, given a low-resolution
observation of the same image in an alternative modality. Our paper offers two
models to address this problem and will be evaluated on the recovery of
high-resolution day images given low-resolution night images of the same scene.
Promising qualitative and quantitative results will be presented for each
model.
