What is different between this fixed version and the original ZeroDCE?

(1) providing 7 different color spaces for training ("RGB", "HSV", "HLS", "YCbCr", "YUV", "LAB", and "LUV").

cd Zero-DCE_code

python lowlight_train.py --channel ("RGB", "HSV", "HLS", "YCbCr", "YUV", "LAB", and "LUV")

(2) providing 7 different color spaces of 200 epochs pretrained weight.

./Zero-DCE_code/snapshots/("RGB", "HSV", "HLS", "YCbCr", "YUV", "LAB", and "LUV").pth

(3) providing applications on videos.

cd Zero-DCE_code

python lowlight_test.py --mode (video/image) --channel ("RGB", "HSV", "HLS", "YCbCr", "YUV", "LAB", and "LUV")

(4) providing a tensorboard to display training loss.

tensorboard --logdir log/train_loss_("RGB", "HSV", "HLS", "YCbCr", "YUV", "LAB", and "LUV")

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

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.

We also provide a MindSpore version of our code: https://pan.baidu.com/s/1uyLBEBdbb1X4QVe2waog_g (passwords: of5l).

Pytorch

Pytorch implementation of Zero-DCE

Requirements

1. Python 3.7
2. Pytorch 1.0.0
3. opencv
4. torchvision 0.2.1
5. cuda 10.0

Zero-DCE does not need special configurations. Just basic environment.

Or you can create a conda environment to run our code like this: conda create --name zerodce_env opencv pytorch==1.0.0 torchvision==0.2.1 cuda100 python=3.7 -c pytorch

Folder structure

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 
├── lowlight_test.py # testing code
├── lowlight_train.py # training code
├── model.py # Zero-DEC network
├── dataloader.py
├── snapshots
│   ├── Epoch99.pth #  A pre-trained snapshot (Epoch99.pth)

Test:

cd Zero-DCE_code

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.

Train:

1. cd Zero-DCE_code

2. download the training data google drive or baidu cloud [password: 1234]

3. unzip and put the downloaded "train_data" folder to "data" folder

python lowlight_train.py 

License

The code is made available for academic research purpose only. Under Attribution-NonCommercial 4.0 International License.

Bibtex

@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}
}

(Full paper: http://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf)

Contact

If you have any questions, please contact ICHEN LU at [email protected].