This is a repository of two papers
Paper1: Enhancing Low-Light Images: A Synthetic Data Perspective on Practical and Generalizable Solutions link
Paper2: Towards Realistic Low-Light Image Enhancement via ISP-Driven Data Modeling link
- Make Conda Environment
conda create -n synllie python=3.7
conda activate synllie
- Install Dependencies
conda install pytorch=1.11 torchvision cudatoolkit=11.3 -c pytorch
pip install matplotlib scikit-learn scikit-image opencv-python yacs joblib natsort h5py tqdm tensorboard
pip install einops gdown addict future lmdb numpy pyyaml requests scipy yapf lpips kornia
- Install BasicSR
python setup.py develop --no_cuda_ext
To download the pre-trained model, follow the link below：
Paper1: Google Drive (There are three folders, including SNRNet, Retinexformer and RetinexMamba trained using our simulation method.)
Paper2: Google Drive (Includes the enhanced results of the datasets)
Put them in folder pretrained_models
Tip: We use the method provided in IQA-PyTorch to calculate the metrics. IQA-PyTorch
# activate the environment
conda activate synllie
# run (The path of the test set is modified in the configuration file.)
# test on LOL_v1
python3 Enhancement/test_from_dataset.py --opt Options/NewAttentionUNet2_test.yml --weights pretrained_models/AttentionUNet_LOL_v1.pth
# test on LOL_v2_real
python3 Enhancement/test_from_dataset.py --opt Options/NewAttentionUNet2_test.yml --weights pretrained_models/AttentionUNet_LOL_v2.pth
# test on other dataset
python3 Enhancement/test_from_dataset.py --opt Options/NewAttentionUNet2_test.yml --weights pretrained_models/AttentionUNet_baseline.pth