In recent years, deep learning has made great progress in many fields such as
image recognition, natural language processing, speech recognition and video
super-resolution. In this survey, we comprehensively investigate 33
state-of-the-art video super-resolution (VSR) methods based on deep learning.
It is well known that the leverage of information within video frames is
important for video super-resolution. Thus we propose a taxonomy and classify
the methods into six sub-categories according to the ways of utilizing
inter-frame information. Moreover, the architectures and implementation details
of all the methods are depicted in detail. Finally, we summarize and compare
the performance of the representative VSR method on some benchmark datasets. We
also discuss some challenges, which need to be further addressed by researchers
in the community of VSR. To the best of our knowledge, this work is the first
systematic review on VSR tasks, and it is expected to make a contribution to
the development of recent studies in this area and potentially deepen our
understanding to the VSR techniques based on deep learning.

该论文系统研究了基于深度学习的 33 种视频超分辨率方法，提出了一个分类法并将这些方法分属到六个子类中，并详细描述了所有方法的体系结构和实现细节。通过在一些基准数据集上对代表性 VSR 方法的性能进行总结和比较，讨论了 VSR 领域中需要进一步研究的一些挑战。这项工作预计对 VSR 技术的深度学习方法做出贡献，从而深化我们对该领域的理解，并成为该领域中最早的系统研究之一。