  Video enhancement is a challenging problem, more than that of stills, mainly
due to high computational cost, larger data volumes and the difficulty of
achieving consistency in the spatio-temporal domain. In practice, these
challenges are often coupled with the lack of example pairs, which inhibits the
application of supervised learning strategies. To address these challenges, we
propose an efficient adversarial video enhancement framework that learns
directly from unpaired video examples. In particular, our framework introduces
new recurrent cells that consist of interleaved local and global modules for
implicit integration of spatial and temporal information. The proposed design
allows our recurrent cells to efficiently propagate spatio-temporal information
across frames and reduces the need for high complexity networks. Our setting
enables learning from unpaired videos in a cyclic adversarial manner, where the
proposed recurrent units are employed in all architectures. Efficient training
is accomplished by introducing one single discriminator that learns the joint
distribution of source and target domain simultaneously. The enhancement
results demonstrate clear superiority of the proposed video enhancer over the
state-of-the-art methods, in all terms of visual quality, quantitative metrics,
and inference speed. Notably, our video enhancer is capable of enhancing over
35 frames per second of FullHD video (1080x1920).
