On the Generalization of BasicVSR++ to Video Deblurring and Denoising [article]

Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy
2022 arXiv   pre-print
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
more » ... al 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.
arXiv:2204.05308v2 fatcat:cesgfgafknh6xliggqndda5qye