Qunxiang / SADNet

Pytorch code for "Spatial-Adaptive Network for Single Image Denoising"

SADNet

By Meng Chang, Qi Li, Huajun Feng, Zhihai Xu

This is the official Pytorch implementation of "Spatial-Adaptive Network for Single Image Denoising" [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

Get Started

Installation

The Deformable ConvNets V2 (DCNv2) module in our code adopts chengdazhi'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 option.py to your own path.
3. You can set any training parameters in option.py. After that, train the model:

cd $SADNet_ROOT
python train.py

Test

1. Download the trained models from Google Drive and place them in /ckpt/.
2. Place the testing dataset in /dataset/test/ or set the testing path in option.py to your own path.
3. Set the parameters in option.py (eg. 'epoch_test', 'gray' and etc.)
4. test the trained models:

cd $SADNet_ROOT
python test.py

Citation

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

@article{chang2020spatial,
  title={Spatial-Adaptive Network for Single Image Denoising},
  author={Chang, Meng and Li, Qi and Feng, Huajun and Xu, Zhihai},
  journal={arXiv preprint arXiv:2001.10291},
  year={2020}
}

Acknowledgments

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