- 2023.11.03 : The same test setting as KinD, LLFlow, and recent diffusion models and the corresponding results on LOL are provided. Please check the
Testing
part for more details. - 2023.11.02 : Retinexformer is added to the Awesome-Transformer-Attention collection. π«
- 2023.10.20 : Params and FLOPS evaluating function is provided. Feel free to check and use it.
- 2023.10.12 : Retinexformer is added to the ICCV-2023-paper collection. π
- 2023.10.10 : Retinexformer is added to the low-level-vision-paper-record collection. β
- 2023.10.06 : Retinexformer is added to the awesome-low-light-image-enhancement collection. π
- 2023.09.20 : Some results on ExDark nighttime object detection are released.
- 2023.09.20 : Code, models, results, and training logs have been released. Feel free to use them. β
- 2023.07.14 : Our paper has been accepted by ICCV 2023. Code and Models will be released. π
-
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 LOL-v1, LOL-v2-real, and LOL-v2-synthetic datasets with the same test setting as KinD, LLFlow, and recent diffusion models 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 -
Results on ExDark nighttime object detection can be downloaded from Baidu Disk (code:
cyh2
) or Google Drive. Please use this repo to run experiments on the ExDark dataset
Performance on LOL with the same test setting as KinD, LLFlow, and diffusion models:
Metric | LOL-v1 | LOL-v2-real | LOL-v2-synthetic |
---|---|---|---|
PSNR | 27.18 | 27.71 | 29.04 |
SSIM | 0.850 | 0.856 | 0.939 |
Please note that we do not suggest this test setting because it uses the mean of the ground truth to obtain better results. But, if you want to follow KinD, LLFlow, and recent diffusion-based works, it is your choice to use this test setting. Please refer to the Testing
part for details.
- 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
Note:
(1) Please use bandizip to jointly unzip the .zip
and .z01
files of SMID, SDSD-indoor, and SDSD-outdoor
(2) Please process the raw images of the MIT Adobe FiveK dataset following the sRGB output mode or directly download and use the sRGB image pairs processed by us in the Baidu Disk (code:cyh2
) and Google Drive
(3) Please download the text_list.txt
from Google Drive or Baidu Disk (code: ggbh
) and then put it into the folder data/SMID/SMID_Long_np/
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
| | | |--text_list.txt
| | | |--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
We provide the same test setting as LLFlow, KinD, and recent diffusion models. Please note that we do not suggest this test setting because it uses the mean of ground truth to enhance the output of the model. But if you want to follow this test setting, just add a --GT_mean
action at the end of the above test command as
# LOL-v1
python3 Enhancement/test_from_dataset.py --opt Options/RetinexFormer_LOL_v1.yml --weights pretrained_weights/LOL_v1.pth --dataset LOL_v1 --GT_mean
# 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 --GT_mean
# 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 --GT_mean
We have provided a function my_summary()
in Enhancement/utils.py
, please use this function to evaluate the parameters and computational complexity of the models, especially the Transformers as
from utils import my_summary
my_summary(RetinexFormer(), 256, 256, 3, 1)
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{Cai_2023_ICCV,
author = {Cai, Yuanhao and Bian, Hao and Lin, Jing and Wang, Haoqian and Timofte, Radu and Zhang, Yulun},
title = {Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {12504-12513}
}
@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.