This is an implementation of the following paper.
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression
European Conference on Computer Vision (ECCV'2022
)
Yeying Jin, Wenhan Yang and Robby T. Tan
[Paper]
[Supplementary]
[Poster]
[Slides]
[Link]
- Light-effects data [Dropbox] | [BaiduPan (code:self)]
Light-effects data is collected from Flickr and by ourselves, with multiple light colors in various scenes.
CVPR2021
Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects [Paper]
Aashish Sharma and Robby T. Tan
- LED data [Dropbox] | [BaiduPan (code:ledl)]
We captured images with dimmer light as the reference images.
- GTA5 nighttime fog [Dropbox] | [BaiduPan (code:67ml)]
Synthetic GTA5 nighttime fog data.
ECCV2020
Nighttime Defogging Using High-Low Frequency Decomposition and Grayscale-Color Networks [Paper]
Wending Yan, Robby T. Tan and Dengxin Dai
- Syn-light-effects [Dropbox] | [BaiduPan (code:synt)]
Synthetic-light-effects data is the implementation of the paper:
ICCV2017
A New Convolution Kernel for Atmospheric Point Spread Function Applied to Computer Vision [Paper]
Run the Matlab code to generate Syn-light-effects:
glow_rendering_code/repro_ICCV2007_Fig5.m
-
LOL dataset
"Deep Retinex Decomposition for Low-Light Enhancement", BMVC, 2018. [Baiduyun (code:sdd0)] | [Google Drive] -
LOL-Real dataset
"Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement", TIP, 2021. [Baiduyun (code:l9xm)] | [Google Drive]
- Download the pre-trained LOL model [Dropbox] | [BaiduPan (code:lol2)], put in
./results/LOL/model/
- Put the test images in
./LOL/
🔥🔥 Online test: https://replicate.com/cjwbw/night-enhancement
python main.py
- LOL-test Results (15 test images) [Dropbox] | [BaiduPan (code:lol1)]
Get the following Table 3 in the main paper on the LOL-test dataset.
Learning | Method | PSNR | SSIM |
---|---|---|---|
Unsupervised Learning | Ours | 21.521 | 0.7647 |
N/A | Input | 7.773 | 0.1259 |
- LOL-Real Results (100 test images) [Dropbox] | [BaiduPan (code:lolc)]
Get the following Table 4 in the main paper on the LOL-Real dataset.
Learning | Method | PSNR | SSIM |
---|---|---|---|
Unsupervised Learning | Ours | 25.51 | 0.8015 |
N/A | Input | 9.72 | 0.1752 |
Re-train (train from scratch) in LOL_V2_real (698 train images), and test on LOL_V2_real [Dropbox] | [BaiduPan (code:lol2)].
PSNR: 20.85 (vs EnlightenGAN's 18.23), SSIM: 0.7243 (vs EnlightenGAN's 0.61).
- Download the pre-trained de-light-effects model [Dropbox] | [BaiduPan (code:dele)], put in
./results/delighteffects/model/
- Put the test images in
./light-effects/
python main_delighteffects.py
Inputs are in ./light-effects/
, Outputs are in ./light-effects-output/
demo_all.ipynb
python demo.py
demo_decomposition.m
Initial Background Results [Dropbox] | Light-Effects Results [Dropbox] | Shading Results [Dropbox] |
---|---|---|
[BaiduPan (code:jjjj)] | [BaiduPan (code:lele)] | [BaiduPan (code:llll)] |
- Run the MATLAB code to adaptively fuse the three color channels, and output
I_gray
.
checkGrayMerge.m
-
Download the fine-tuned VGG model [Dropbox] | [BaiduPan (code:dark)] (fine-tuned on ExDark), put in
./VGG_code/ckpts/vgg16_featureextractFalse_ExDark/nets/model_best.tar
-
Obtain structure features.
python test_VGGfeatures.py
The code and models in this repository are licensed under the MIT License for academic and other non-commercial uses.
For commercial use of the code and models, separate commercial licensing is available. Please contact:
- Yeying Jin ([email protected])
- Robby T. Tan ([email protected])
- Jonathan Tan ([email protected])
If this work is useful for your research, please cite our paper.
@inproceedings{jin2022unsupervised,
title={Unsupervised night image enhancement: When layer decomposition meets light-effects suppression},
author={Jin, Yeying and Yang, Wenhan and Tan, Robby T},
booktitle={European Conference on Computer Vision},
pages={404--421},
year={2022},
organization={Springer}
}
@inproceedings{jin2023enhancing,
title={Enhancing visibility in nighttime haze images using guided apsf and gradient adaptive convolution},
author={Jin, Yeying and Lin, Beibei and Yan, Wending and Yuan, Yuan and Ye, Wei and Tan, Robby T},
booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
pages={2446--2457},
year={2023}
}
If light-effects data is useful for your research, please cite the paper.
@inproceedings{sharma2021nighttime,
title={Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects},
author={Sharma, Aashish and Tan, Robby T},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11977--11986},
year={2021}
}