In recent years, deep learning has made great progress in the fields of image recognition, video analysis, natural language processing and speech recognition, including video super-resolution tasks. In this survey, we comprehensively investigate 28 state-of-the-art video super-resolution methods based on deep learning. It is well known that the leverage of information within video frames is important for video super-resolution. Hence 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 (including input and output, loss function and learning rate) of all the methods are depicted in details. Finally, we summarize and compare their performance on some benchmark datasets under different magnification factors. We also discuss some challenges, which need to be further addressed by researchers in the community of video super-resolution. Therefore, this work is expected to make a contribution to the future development of research in video super-resolution, and alleviate understandability and transferability of existing and future techniques into practice.

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。

基于深度学习的视频超分辨率技术综述