论文标题
BASICVSR：在视频超级分辨率及以后寻找基本组成部分
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
论文作者
论文摘要
视频超分辨率（VSR）方法往往比图像对应物具有更多的组件，因为它们需要利用额外的时间维度。复杂的设计并不少见。在这项研究中，我们希望解开结，并重新考虑以四个基本功能为指导的VSR的一些最重要的组成部分，即传播，对准，聚合和提升采样。通过重复一些现有的组件，添加了最少的重新设计，我们显示了简洁的管道，BASICVSR，与许多最先进的算法相比，速度和恢复质量方面取得了吸引人的改进。我们进行系统分析，以解释如何获得这种增益并讨论陷阱。我们通过提出信息填充机制和耦合传播方案来进一步显示基本VSR的可扩展性，以促进信息聚合。基本VSR及其扩展ICONVSR可以作为未来VSR方法的强大基准。
Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.
