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description=Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches.;
keywords=video, super resolution, video restoration, BasicVSR;

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basicvsr the search for essential components in video super resolution and beyond basicvsr the search for essential components in video super resolution and beyond kelvin c k chan 1 xintao wang 2 ke yu 3 chao dong 4 chen change loy 1 1 s lab nanyang technological university 2 applied research center tencent pcg 3 cuhk sensetime joint lab the chinese university of hong kong 4 shenzhen institutes of advanced technology chinese academy of sciences materials network details results citation related work the basicvsr framework basicvsr is a simple yet efficient baseline for video super resolution it consists of only generic components such as residual blocks and optical flow but outperforms existing state of the arts with fewer parameters and faster inference speed its simplicity enables the possibility of serving as a backbone where sophisticated components can be added to it for further improvement highlights basicvsr outperforms existing works with fewer parameters and faster speed it can serve as a strong baseline for video super resolution we build upon basicvsr and propose iconvsr with two novel components it outperforms our strong basicvsr with similar speed materials paper codes abstract video super resolution vsr approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension complex designs are not uncommon in this study we wish to untangle the knots and reconsider some most essential components for vsr guided by four basic functionalities i e propagation alignment aggregation and upsampling by reusing some existing components added with minimal redesigns we show a succinct pipeline basicvsr that achieves appealing improvements in terms of speed and restoration quality in comparison to many state of the art algorithms we conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls we further show the extensibility of basicvsr by presenting an information refill mechanism and a coupled propagation scheme to facilitate information aggregation the basicvsr and its extension iconvsr can serve as strong baselines for future vsr approaches network details and iconvsr left the propagation branches in basicvsr and iconvsr right the proposed components in iconvsr see our paper for more details results results of 4x super resolution basicvsr and iconvsr reconstruct finer details when compared to existing state of the arts with information refill and coupled propagation iconvsr further restores sharper edges and more details citation inproceedings chan2021basicvsr author chan kelvin ck and wang xintao and yu ke and dong chao and loy chen change title basicvsr the search for essential components in video super resolution and beyond booktitle proceedings of the ieee conference on computer vision and pattern recognition year 2021 related work basicvsr basicvsr is built upon our basicvsr with improvements in propgation and alignment it obtains three champions and one runner up in ntire 2021 video restoration and enhancement challenge glean glean is our attempt in image super resolution that leverages a pre trained stylegan as a generative latent bank contact if you have any question please contact kelvin chan at chan0899 e ntu edu sg