  With the advent of perceptual loss functions, new possibilities in
super-resolution have emerged, and we currently have models that successfully
generate near-photorealistic high-resolution images from their low-resolution
observations. Up to now, however, such approaches have been exclusively limited
to single image super-resolution. The application of perceptual loss functions
on video processing still entails several challenges, mostly related to the
lack of temporal consistency of the generated images, i.e., flickering
artifacts. In this work, we present a novel adversarial recurrent network for
video upscaling that is able to produce realistic textures in a temporally
consistent way. The proposed architecture naturally leverages information from
previous frames due to its recurrent architecture, i.e. the input to the
generator is composed of the low-resolution image and, additionally, the warped
output of the network at the previous step. Together with a video
discriminator, we also propose additional loss functions to further reinforce
temporal consistency in the generated sequences. The experimental validation of
our algorithm shows the effectiveness of our approach which obtains images with
high perceptual quality and improved temporal consistency.
