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

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 se...

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Bibliographic Details
Published inProceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 5962 - 5971
Main Authors Chan, Kelvin C.K., Zhou, Shangchen, Xu, Xiangyu, Loy, Chen Change
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2022
Subjects
Computational modeling
Computer vision
Low-level vision
Pattern recognition
Spatiotemporal phenomena
Superresolution
Task analysis
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Summary: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.
ISSN:1063-6919
DOI:10.1109/CVPR52688.2022.00588