  A recurrent structure is a popular framework choice for the task of video
super-resolution. The state-of-the-art method BasicVSR adopts bidirectional
propagation with feature alignment to effectively exploit information from the
entire input video. In this study, we redesign BasicVSR by proposing
second-order grid propagation and flow-guided deformable alignment. We show
that by empowering the recurrent framework with the enhanced propagation and
alignment, one can exploit spatiotemporal information across misaligned video
frames more effectively. The new components lead to an improved performance
under a similar computational constraint. In particular, our model BasicVSR++
surpasses BasicVSR by 0.82 dB in PSNR with similar number of parameters. In
addition to video super-resolution, BasicVSR++ generalizes well to other video
restoration tasks such as compressed video enhancement. In NTIRE 2021,
BasicVSR++ obtains three champions and one runner-up in the Video
Super-Resolution and Compressed Video Enhancement Challenges. Codes and models
will be released to MMEditing.
