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(TIP2021) Deep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images

Official code for TIP2021 paper: Deep-Masking Generative Network:A Unified Framework for Background Restoration from Superimposed Images.

Tasks include image reflection removal, image deraining, and image dehazing.

Network Architecture

​ The overall framework of our proposed DMGN.

Installation

The model is built in PyTorch 1.2.0 and tested on Ubuntu 16.04 environment (Python3.7, CUDA9.0, cuDNN7.5).

You can installation the environment via the following:

pip install -r requirements.txt

Testing

First, you should select the model of one task in line 18 eval.py.

Download the pre-trained models here.

Put the downloaded .pth files into ./checkpoints/ .

python eval.py

Citation

If you find this work useful for your research, please cite:

@article{feng2021deep,
  title={Deep-masking generative network: A unified framework for background restoration from superimposed images},
  author={Feng, Xin and Pei, Wenjie and Jia, Zihui and Chen, Fanglin and Zhang, David and Lu, Guangming},
  journal={IEEE Transactions on Image Processing},
  volume={30},
  pages={4867--4882},
  year={2021},
  publisher={IEEE}
}