We relese our testing code first. The Zero-DCE is in the product, so we may release the training codes late.
You can find more details here: https://li-chongyi.github.io/Proj_Zero-DCE.html. Have fun!
The implementation of Zero-DCE is for non-commercial use only.
Pytorch implementation of Zero-DCE
- Python 3
- Pytorch
Zero-DCE does not need special configurations. Just basic environment.
Download the Zero-DCE_code first. The following shows the basic folder structure.
├── data
│ ├── test_data # testing data. You can make a new folder for your testing data, like LIME, MEF, and NPE.
│ │ ├── LIME
│ │ └── MEF
│ │ └── NPE
│ └── train_data # will release soon
├── lowlight_test.py # testing code
├── model.py # Zero-DEC network
├── dataloader.py
├── snapshots
│ ├── Epoch99.pth # A pre-trained snapshot (Epoch99.pth)
python lowlight_test.py
The script will process the images in the sub-folders of "test_data" folder and make a new folder "result" in the "data". You can find the enhanced images in the "result" folder.
@inproceedings{Zero-DCE,
author = {Guo, Chunle Guo and Li, Chongyi and Guo, Jichang and Loy, Chen Change and Hou, Junhui and Kwong, Sam and Cong, Runmin},
title = {Zero-reference deep curve estimation for low-light image enhancement},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)},
pages = {1780-1789},
month = {June},
year = {2020}
}
If you have any questions, please contact Chongyi Li at lichongyi25@gmail.com or Chunle Guo at guochunle@tju.edu.cn.