Awesome
URetinex-Net: Retinex-based Deep Unfolding Network for Low-light-Image-Enhancement
Official PyTorch implementation of URetinex-Net: Retinex-based Deep Unfolding Network for Low-light-Image-Enhancement in CVPR 2022.
[Paper] [Supplementary] [Video]
Requirements
- Python == 3.7.6
- PyTorch == 1.4.0
- torchvision == 0.5.0
Test
If you only want to process a single image, just run like this (you can specify your image path)
python test.py --img_path "./demo/input/img.png"
Enhance results will be saved in ./demo/output if output_path
is not specified!
Evaluate
If you want to evaluate using our provided pretrained model, please download the LOL test datasets. And arrange the dataset as ./test_data/LOLdataset/eval15. Then simply run
python evaluate.py
Citation
If you find our work useful, please cite our paper by the following:
@InProceedings{Wu_2022_CVPR,
author = {Wu, Wenhui and Weng, Jian and Zhang, Pingping and Wang, Xu and Yang, Wenhan and Jiang, Jianmin},
title = {URetinex-Net: Retinex-Based Deep Unfolding Network for Low-Light Image Enhancement},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {5901-5910}
}
Noted that the code is only for non-commercial use! should you have any queries, contact me at wj1997s@163.com