LSFNet (TMM)

Pytorch code for "Low-light Image Restoration with Short- and Long-exposure Raw Pairs" [Paper]

(Noting: The source code is a coarse version for reference and the model provided may not be optimal.)

Prerequisites

• Python 3.6
• Pytorch 1.1
• CUDA 9.0
• Rawpy 0.13.1

Get Started

Installation

The Deformable ConvNets V2 (DCNv2) module in our code adopts EDVR's implementation.

You can compile the code according to your machine.

cd ./dcn
python setup.py develop

Please make sure your machine has a GPU, which is required for the DCNv2 module.

Train

1. Download the training dataset and use gen_dataset.py to package them in the h5py format.
2. Place the h5py file in /Dataset/train/ or set the 'src_path' in train.py to your own path.
3. You can set any training parameters in train.py. After that, train the model:

cd $LSFNet_ROOT
python train.py

Test

1. Download the trained models (uploading soon) and place them in /ckpt/.
2. use gen_valid_dataset.py to package them in the h5py format
3. Place the testing dataset in /Dataset/test/ or set the testing path in test_syn.py to your own path.
4. Set the parameters in test_syn.py
5. test the trained models:

cd $LSFNet_ROOT
python test_syn.py

Citation

If you find the code helpful in your research or work, please cite the following papers.

@article{chang2021low,
  title={Low-light Image Restoration with Short-and Long-exposure Raw Pairs},
  author={Chang, Meng and Feng, Huajun and Xu, Zhihai and Li, Qi},
  journal={IEEE Transactions on Multimedia},
  year={2021},
  publisher={IEEE}
}

Acknowledgments

The DCNv2 module in our code adopts from EDVR's implementation.