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<h2 align="center">AGLLNet: Attention Guided Low-light Image Enhancement (IJCV 2021) </h2>

This is the test code for “Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset” in IJCV 2021, by Feifan Lv, Yu Li, and Feng Lu.

<p align="center"><img src="http://yu-li.github.io/paper/lv_ijcv2021.jpg" height="240"/></p>

<p align="center">Paper | ArXiv | Project page (data)</p>

Requirements

Usage

Testing

You can put you image into the folder input and run

cd AGLLNet
python run_agllnet.py

The results will be stored in the folder output.

Training:

Training code will NOT be provided this time.

Model

Bibtex

If you use this code for your research, please consider star this repo and cite our paper.

@article{lv2021attention,
 title={Attention guided low-light image enhancement with a large scale low-light simulation dataset},
 author={Lv, Feifan and Li, Yu and Lu, Feng},
 journal={International Journal of Computer Vision},
 volume={129},
 number={7},
 pages={2175--2193},
 year={2021}
}

Related work: stable low light video enhancement

Learning Temporal Consistency for Low Light Video Enhancement from Single Images (CVPR2021) Paper | Code