BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

By Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy. Computer Vision and Pattern Recognition, 2021

642 citations recorded by the source corpus.

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 re-current framework with enhanced propagation and align-ment, one can exploit spatiotemporal information across misaligned video frames more effectively. The new components lead to an improved performance under a simi-lar computational constraint. In particular, our model Ba-sicVSR++ surpasses BasicVSR by a significant 0.82 dB in PSNR with similar number of parameters. BasicVSR++ is generalizable to other video restoration tasks, and obtains three champions and one first runner-up in NTIRE 2021 video restoration challenge.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c2cfc2d356f59ce2615c68b913d83cbae313a5b0

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Citations in the corpus

LaVie: High-Quality Video Generation with Cascaded Latent Diffusion Models (2023, neutral): “The Bund, Shanghai, with the ship moving on the river, oil painting.”
VRT: A Video Restoration Transformer (2022, methodology): “2) Temporal Alignment and Fusion: Since supporting frames are often highly-related but misaligned, temporal alignment plays an critical role in video restoration [2], [3], [12], [13], [31].”