- 2023.09.20 : Code, models, results, and training logs have been released. Feel free to use them. ⭐
- 2023.07.14 : Our paper has been accepted by ICCV 2023. Code and Models will be released. 🚀
- Results on LOL-v1, LOL-v2-real, LOL-v2-synthetic, SID, SMID, SDSD-in, SDSD-out, and MIT Adobe FiveK datasets can be downloaded from Baidu Disk (code:
cyh2) or Google Drive - Results on LIME, NPE, MEF, DICM, and VV datasets can be downloaded from Baidu Disk (code:
cyh2) or Google Drive
Performance on LOL-v1, LOL-v2-real, LOL-v2-synthetic, SID, SMID, SDSD-in, and SDSD-out:
Performance on MIT Adobe FiveK:
Performance on LIME, NPE, MEF, DICM, and VV:
- Make Conda Environment
conda create -n Retinexformer python=3.7
conda activate Retinexformer
- 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
- Install BasicSR
python setup.py develop --no_cuda_ext
Download the following datasets:
LOL-v1 Baidu Disk (code: cyh2), Google Drive
LOL-v2 Baidu Disk (code: cyh2), Google Drive
SID Baidu Disk (code: gplv), Google Drive
SMID Baidu Disk (code: btux), Google Drive
SDSD-indoor Baidu Disk (code: jo1v), Google Drive
SDSD-outdoor Baidu Disk (code: uibk), Google Drive
MIT-Adobe FiveK Baidu Disk (code:cyh2), Google Drive, Official
Please process the MIT Adobe FiveK dataset following the sRGB setting
Then organize these datasets as follows:
|--data
| |--LOLv1
| | |--Train
| | | |--input
| | | | |--100.png
| | | | |--101.png
| | | | ...
| | | |--target
| | | | |--100.png
| | | | |--101.png
| | | | ...
| | |--Test
| | | |--input
| | | | |--111.png
| | | | |--146.png
| | | | ...
| | | |--target
| | | | |--111.png
| | | | |--146.png
| | | | ...
| |--LOLv2
| | |--Real_captured
| | | |--Train
| | | | |--Low
| | | | | |--00001.png
| | | | | |--00002.png
| | | | | ...
| | | | |--Normal
| | | | | |--00001.png
| | | | | |--00002.png
| | | | | ...
| | | |--Test
| | | | |--Low
| | | | | |--00690.png
| | | | | |--00691.png
| | | | | ...
| | | | |--Normal
| | | | | |--00690.png
| | | | | |--00691.png
| | | | | ...
| | |--Synthetic
| | | |--Train
| | | | |--Low
| | | | | |--r000da54ft.png
| | | | | |--r02e1abe2t.png
| | | | | ...
| | | | |--Normal
| | | | | |--r000da54ft.png
| | | | | |--r02e1abe2t.png
| | | | | ...
| | | |--Test
| | | | |--Low
| | | | | |--r00816405t.png
| | | | | |--r02189767t.png
| | | | | ...
| | | | |--Normal
| | | | | |--r00816405t.png
| | | | | |--r02189767t.png
| | | | | ...
| |--SDSD
| | |--indoor_static_np
| | | |--input
| | | | |--pair1
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | |--pair2
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | ...
| | | |--GT
| | | | |--pair1
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | |--pair2
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | ...
| | |--outdoor_static_np
| | | |--input
| | | | |--MVI_0898
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | |--MVI_0918
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | ...
| | | |--GT
| | | | |--MVI_0898
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | |--MVI_0918
| | | | | |--0001.npy
| | | | | |--0002.npy
| | | | | ...
| | | | ...
| |--SID
| | |--short_sid2
| | | |--00001
| | | | |--00001_00_0.04s.npy
| | | | |--00001_00_0.1s.npy
| | | | |--00001_01_0.04s.npy
| | | | |--00001_01_0.1s.npy
| | | | ...
| | | |--00002
| | | | |--00002_00_0.04s.npy
| | | | |--00002_00_0.1s.npy
| | | | |--00002_01_0.04s.npy
| | | | |--00002_01_0.1s.npy
| | | | ...
| | | ...
| | |--long_sid2
| | | |--00001
| | | | |--00001_00_0.04s.npy
| | | | |--00001_00_0.1s.npy
| | | | |--00001_01_0.04s.npy
| | | | |--00001_01_0.1s.npy
| | | | ...
| | | |--00002
| | | | |--00002_00_0.04s.npy
| | | | |--00002_00_0.1s.npy
| | | | |--00002_01_0.04s.npy
| | | | |--00002_01_0.1s.npy
| | | | ...
| | | ...
| |--SMID
| | |--SMID_LQ_np
| | | |--0001
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | |--0002
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | ...
| | |--SMID_Long_np
| | | |--0001
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | |--0002
| | | | |--0001.npy
| | | | |--0002.npy
| | | | ...
| | | ...
| |--FiveK
| | |--train
| | | |--input
| | | | |--a0099-kme_264.jpg
| | | | |--a0101-kme_610.jpg
| | | | ...
| | | |--target
| | | | |--a0099-kme_264.jpg
| | | | |--a0101-kme_610.jpg
| | | | ...
| | |--test
| | | |--input
| | | | |--a4574-DSC_0038.jpg
| | | | |--a4576-DSC_0217.jpg
| | | | ...
| | | |--target
| | | | |--a4574-DSC_0038.jpg
| | | | |--a4576-DSC_0217.jpg
| | | | ...
We also provide download links for LIME, NPE, MEF, DICM, and VV datasets that have no ground truth:
Baidu Disk (code: cyh2)
or Google Drive
Download our models from Baidu Disk (code: cyh2) or Google Drive. Put them in folder pretrained_weights
# LOL-v1
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_LOL_v1.yml --weights pretrained_weights/LOL_v1.pth --dataset LOL_v1
# LOL-v2-real
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_LOL_v2_real.yml --weights pretrained_weights/LOL_v2_real.pth --dataset LOL_v2_real
# LOL-v2-synthetic
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_LOL_v2_synthetic.yml --weights pretrained_weights/LOL_v2_synthetic.pth --dataset LOL_v2_synthetic
# SID
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_SID.yml --weights pretrained_weights/SID.pth --dataset SID
# SMID
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_SMID.yml --weights pretrained_weights/SMID.pth --dataset SMID
# SDSD-indoor
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_SDSD_indoor.yml --weights pretrained_weights/SDSD_indoor.pth --dataset SDSD_indoor
# SDSD-outdoor
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_SDSD_outdoor.yml --weights pretrained_weights/SDSD_outdoor.pth --dataset SDSD_outdoor
# FiveK
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_FiveK.yml --weights pretrained_weights/FiveK.pth --dataset FiveK
Feel free to check our training logs from Baidu Disk (code: cyh2) or Google Drive
# LOL-v1
python3 basicsr/train.py --opt Options/RetinexFormer_LOL_v1.yml
# LOL-v2-real
python3 basicsr/train.py --opt Options/RetinexFormer_LOL_v2_real.yml
# LOL-v2-synthetic
python3 basicsr/train.py --opt Options/RetinexFormer_LOL_v2_synthetic.yml
# SID
python3 basicsr/train.py --opt Options/RetinexFormer_SID.yml
# SMID
python3 basicsr/train.py --opt Options/RetinexFormer_SMID.yml
# SDSD-indoor
python3 basicsr/train.py --opt Options/RetinexFormer_SDSD_indoor.yml
# SDSD-outdoor
python3 basicsr/train.py --opt Options/RetinexFormer_SDSD_outdoor.yml
# FiveK
python3 basicsr/train.py --opt Options/RetinexFormer_FiveK.yml
@inproceedings{retinexformer,
title={Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement},
author={Yuanhao Cai and Hao Bian and Jing Lin and Haoqian Wang and Radu Timofte and Yulun Zhang},
booktitle={ICCV},
year={2023}
}Acknowledgment: Our code is based on the BasicSR toolbox.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent