FRR-NET-a-fast-reparameterized-residual-network-for-low-light-image-enhancement

[Paper]

Representitive Results

Training process

run 'train.py'

Testing process

We provide two pre-trained models. Mainly for different number of channels. The dataset is consistent with that described in the paper and is a collection of the LOL dataset and the FiveK dataset. You can also train with your own dataset. Modify your pre-trained model path in 'data_test.py

If you find this work useful for you, please cite

@article{chen2024frr,
  title={FRR-NET: a fast reparameterized residual network for low-light image enhancement},
  author={Chen, Yuhan and Zhu, Ge and Wang, Xianquan and Yang, Huan},
  journal={Signal, Image and Video Processing},
  pages={1--10},
  year={2024},
  publisher={Springer}
}

Contact

If you have any questions, please contact Yuhan Chen at cyh1217552389@gmail.com