by Hue Nguyen, Diep Tran, Khoi Nguyen, Rang Nguyen

The extremes of lighting (e.g. too much or too little light) usually cause many troubles for machine and human vision. Many recent works have mainly focused on under-exposure cases where images are often captured in low-light conditions (e.g. nighttime) and achieved promising results for enhancing the quality of images. However, they are inferior to handling images under over-exposure. To mitigate this limitation, we propose a novel unsupervised enhancement framework which is robust against various lighting conditions while doesn't require any well-exposed images to serve as the ground-truths. Our main concept is to construct pseudo-ground-truth images synthesized from multiple source images that simulate all potential exposure scenarios to train the enhancement network. Our extensive experiments show that the proposed approach consistently outperforms the current state-of-the-art unsupervised counterparts in several public datasets in terms of both quantitative metrics and qualitative results.

Details of our framework and benchmark results can be found in our paper:

@inproceedings{hue2023psenet,
 author={Hue Nguyen and Diep Tran and Khoi Nguyen and Rang Nguyen},
 booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
 title={PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement},
 year= {2023}
}

Please CITE our paper when this repository is used to help produce published results or is incorporated into other software.

1. Clone this repository

git clone https://github.com/VinAIResearch/PSENet-Image-Enhancement.git
cd PSENet-Image-Enhancement

2. Setup the environment

conda create -n psenet python=3.7
conda activate psenet
pip install -r requirements.txt

For a quick demo, please run the following command

cd source
python demo.py 

Please refer to the below links for downloading datasets.

1. SICE dataset

• training set: the customed version of SICE part 1 dataset introduced in ZeroDCE paper.
• testing set: SICE part 2 dataset. Note that the results reported in the main paper are evaluated on resized images with 0.25 of their original size due to memory constraints.

2. Afifi dataset
3. LOL dataset (for evaluation only): Both training and evaluation sets are used for evaluation in the main paper.

Unzip all downloaded datasets to data_root folder to match with the following data structure.

PSENet-Image-Enhancement
├─ assets
├─ data_root
│  ├─ LOLdataset.zip
│  ├─ afifi
│  │  ├─ testing
│  │  │  ├─ INPUT_IMAGES
│  │  │  └─ expert_e_testing_set
│  │  └─ training
│  │     └─ INPUT_IMAGES
│  ├─ lol
│  │  ├─ eval15
│  │  │  ├─ high
│  │  │  └─ low
│  │  └─ our485
│  │     ├─ high
│  │     └─ low
│  └─ sice
│     └─ Dataset_Part2
│        ├─ 1
│        ├─ 2
│        ├─ ...
│        └─ Label
│           ├─ 1.JPG
│           ├─ 2.JPG
|           └─ ...
├─ pretrained
├─ source
├─ README.md
├─ requirements.txt
├─ LICENSE
└─ .gitignore

1. SICE dataset

cd source
python main.py --config configs/sice.yaml

2. Afifi dataset

cd source
python main.py --config configs/afifi.yaml

For custom config, please check the comment in source/configs/sice.yaml

Our pretrained models are provided in folder pretrained. To reproduce the quantitative results reported in the main paper, please run the following commands.

1. SICE dataset

cd source
python main.py --config configs/sice.yaml --pipeline test --checkpoint ../pretrained/sice.pth 

2. Afifi dataset

cd source
python main.py --config configs/afifi.yaml --pipeline test --checkpoint ../pretrained/afifi.pth 

3. LOL dataset

cd source
python main.py --config configs/lol.yaml --pipeline test --checkpoint ../pretrained/sice.pth 

The image enhancement model is built upon MobileNetV3_PyTorch. We thank the authors for providing helpful functions in our work.

If you have any questions or suggestions about this repo, please feel free to contact me ([email protected]).