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XunpengYi / LLIEFormer

Official Code of LLIEFormer: A Low-light Image Enhancement Transformer Network with a Degraded Restoration Model
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[ICIP 2023] LLIEFormer: A Low-Light Image Enhancement Transformer Network with a Degraded Restoration Model

Paper | Code

LLIEFormer: A Low-Light Image Enhancement Transformer Network with a Degraded Restoration Model Xunpeng Yi, Yuxuan Wang, Yizhen Zhao, Jia Yan, Weixia Zhang in ICIP 2023

Pretrained Model

We provide the pretrained models:

Method PSNR SSIM
LLIEFormer 22.08 0.883
Method PSNR SSIM
Zero-DCE 16.90 0.678
KinD 17.27 0.645
KinD++ 18.52 0.701
LIME 18.19 0.671
RUAS 17.91 0.633
RetinexNet 16.71 0.626
LLIEFormer 25.14 0.797

Dataset

You can put and rename the dataset in the following way:

    dataset/
        LOLdataset/
            train/
                high/
                low/
            eval/
                high/
                low/

Test

cd LLIEFormer-main/
CUDA_VISIBLE_DEVICES=0 python test.py

Train

cd LLIEFormer-main/
CUDA_VISIBLE_DEVICES=0 python train.py

Citation

If you find our work useful for your research, please cite our paper

@inproceedings{yi2023llieformer,
  title={Llieformer: A Low-Light Image Enhancement Transformer Network with a Degraded Restoration Model},
  author={Yi, Xunpeng and Wang, Yuxuan and Zhao, Yizhen and Yan, Jia and Zhang, Weixia},
  booktitle={2023 IEEE International Conference on Image Processing (ICIP)},
  pages={1195--1199},
  year={2023},
  organization={IEEE}
}