Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images

This repository contains the implementation of the following paper:

Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images
Seonggwan Ko*, Jinsun Park*, Byungjoo Chae and Donghyeon Cho
Signal Processing Letters

Overview

Visual results

Requirements

The following packages must be installed to perform the proposed model:

• PyTorch 1.7.1
• torchvision 0.8.2
• Pillow 8.2.0
• TensorBoardX 2.2
• tqdm

Test

Test datasets should be arranged as the following folder dataset/test.

dataset
│   ├── test
│   │   ├── LIME
│   │   ├── LOL
│   │   ├── DICM
│   │   └── ...
└── ...

If you set up the folder, you can make it run.

python test.py

Train

To train the proposed model, the following options are required:

python train.py --lowlight_images_path 'your_dataset_path' --gt_images_path 'your_GT_dataset_path' --pretrain_dir  'your_pretrain_path'

lowlight_images_path is the path of your low-light image

gt_images_path is the path of your ground-truth image

pretrain_dir is the path of your pretrained teacher model path

Dataset

We provide 10,000 training pairs and 387 test images.

Please click here if you want to download our dataset.

Dataset Creation

• We collected 25,967 low-light images from BDD100k(4,830 images) and Dark Zurich(5,336 images), LoLi-Phone(6,442 images), ExDark(7,263 images), SICE(1,611), LOL(485 images).
• Then, we generate pseudo well-exposed images using the pretrained EnlightenGAN, and additionally reduce noise using DnCNN.

Citation

 @ARTICLE{,
  author={S. {Ko} and J. {Park} and B. {Chae} and D. {Cho}},
  journal={IEEE Signal Processing Letters}, 
  title={Learning Lightweight Low-Light Enhancement Network using Pseudo Well-Exposed Images}, 
  year={2021}
}

License and Acknowledgement

The code framework is mainly modified from Zero-DCE, AdaBelief and SPKD. Please refer to the original repo for more usage and documents. Thanks to authors for sharing the codes!