  The exploitation of long-term information has been a long-standing problem in
video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable
performance in video super-resolution through long-term propagation and
effective alignment. Their success has led to a question of whether they can be
transferred to different video restoration tasks. In this work, we extend
BasicVSR++ to a generic framework for video restoration tasks. In tasks where
inputs and outputs possess identical spatial size, the input resolution is
reduced by strided convolutions to maintain efficiency. With only minimal
changes from BasicVSR++, the proposed framework achieves compelling performance
with great efficiency in various video restoration tasks including video
deblurring and denoising. Notably, BasicVSR++ achieves comparable performance
to Transformer-based approaches with up to 79% of parameter reduction and 44x
speedup. The promising results demonstrate the importance of propagation and
alignment in video restoration tasks beyond just video super-resolution. Code
and models are available at https://github.com/ckkelvinchan/BasicVSR_PlusPlus.

Computer Vision and Pattern Recognition