Awesome
Semi-supervised Transfer Learning for Image Rain Removal
This package contains the Python implementation of "Semi-supervised Transfer Learning for Image Rain Removal", in CVPR 2019.
Usage
Prepare Training Data
Download synthesized data from here, as supervised training data. Put input images in './data/rainy_image_dataset/input' and ground truth images in './data/rainy_image_dataset/label'. Run /data/generate.m to generate HDF files as training data.
Train
python training.py
Test
python testing.py
Cite
Please cite this paper if you use the code:
@InProceedings{Wei_2019_CVPR,
author = {Wei, Wei and Meng, Deyu and Zhao, Qian and Xu, Zongben and Wu, Ying},
title = {Semi-Supervised Transfer Learning for Image Rain Removal},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition},
year = {2019}
}
The prior work using MoG for video deraining can be found here:
@InProceedings{wei2017should,
title={Should we encode rain streaks in video as deterministic or stochastic?},
author={Wei, Wei and Yi, Lixuan and Xie, Qi and Zhao, Qian and Meng, Deyu and Xu, Zongben},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
year={2017}
}
Acknowledge
We use Deep Detail Network as our baseline. Thanks for sharing the code!
Note
- You are welcomed to add more real data.
- You are welcomed to try more recent derain network as baseline.