  This paper studies the problem of real-world video super-resolution (VSR) for
animation videos, and reveals three key improvements for practical animation
VSR. First, recent real-world super-resolution approaches typically rely on
degradation simulation using basic operators without any learning capability,
such as blur, noise, and compression. In this work, we propose to learn such
basic operators from real low-quality animation videos, and incorporate the
learned ones into the degradation generation pipeline. Such
neural-network-based basic operators could help to better capture the
distribution of real degradations. Second, a large-scale high-quality animation
video dataset, AVC, is built to facilitate comprehensive training and
evaluations for animation VSR. Third, we further investigate an efficient
multi-scale network structure. It takes advantage of the efficiency of
unidirectional recurrent networks and the effectiveness of sliding-window-based
methods. Thanks to the above delicate designs, our method, AnimeSR, is capable
of restoring real-world low-quality animation videos effectively and
efficiently, achieving superior performance to previous state-of-the-art
methods. Codes and models are available at
https://github.com/TencentARC/AnimeSR.
