Awesome
Efficient Video Deblurring Guided by Motion Magnitude
Official repository of MMP-RNN (ECCV 2022) Paper
Estimate pixel-wise blur level (MMP) first and video deblur.
Requirements
Pytorch 1.8, Cuda 11.1
lmdb, tqdm, thop, scipy, opencv, scikit-image, tensorboard
Preparing ground truth motion magnitude prior
The ground truth MMP is generated from high-frequency sharp frames during exposure time. Optical flows between sharp frames are estimated by RAFT.
<div align="center"><img src="https://user-images.githubusercontent.com/11170161/178949402-2de1df49-4fd8-481c-a4c2-8a0c534fa0fe.png" width="480"></div>Learning MMP
Using a UNet-like structure to learn MMP.
MMP-RNN
Ultilizing MMP for video deblurring by merging into an RNN.
Citation
@inproceedings{wang2022MMP,
title={Efficient Video Deblurring Guided by Motion Magnitude},
author={Wang, Yusheng and Lu, Yunfan and Gao, Ye and Wang, Lin and Zhong, Zhihang and Zheng, Yinqiang and Yamashita, Atsushi},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
year={2022}
}
Contact
Email of Yusheng Wang: wang@robot.t.u-tokyo.ac.jp