@inproceedings{f881605a346044c1800abc349f370a57,
title = "Video Frame Interpolation Via Residue Refinement",
abstract = "Video frame interpolation achieves temporal super-resolution by generating smooth transitions between frames. Although great success has been achieved by deep neural networks, the synthesized images stills suffer from poor visual appearance and unsatisfactory artifacts. In this paper, we propose a novel network structure that leverages residue refinement and adaptive weight to synthesize in-between frames. The residue refinement technique is used for optical flow and image generation for higher accuracy and better visual appearance, while the adaptive weight map combines the forward and backward warped frames to reduce the artifacts. Moreover, all submodules in our method are implemented by U-Net with less depths, so the efficiency is guaranteed. Experiments on public datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art approaches.",
keywords = "U-Net, Video frame interpolation, adaptive weight map, residue refinement",
author = "Haopeng Li and Yuan Yuan and Qi Wang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",
year = "2020",
month = may,
doi = "10.1109/ICASSP40776.2020.9053987",
language = "英语",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2613--2617",
booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",
}