RFDNet Super Resolution

Residual Feature Distillation Network for Lightweight Image Super-Resolution

Content

• RFDNet
• Getting Started
• Running
• References
• Citations

Getting Started

• Clone the repository

Prerequisites

• Tensorflow 2.2.0+
• Python 3.6+
• Keras 2.3.0
• PIL
• numpy

pip install -r requirements.txt

Running

Training

• Train RFDNet

  python main.py
  

• Test RFDNet

  python test.py
  

Usage

Testing

usage: test.py [-h] [--test_path TEST_PATH] [--gpu GPU]
               [--weight_test_path WEIGHT_TEST_PATH] [--filter FILTER]
               [--feat FEAT] [--scale SCALE]

optional arguments:
                    -h, --help            show this help message and exit
                    --test_path TEST_PATH
                    --gpu GPU
                    --weight_test_path WEIGHT_TEST_PATH
                    --filter FILTER
                    --feat FEAT
                    --scale SCALE

Result

Input - Low Res	Bilinear	Output High Res

License

This project is licensed under the MIT License - see the LICENSE file for details

References

[1] Training and Testing dataset - link

Citation

@misc{liu2020residual,
      title={Residual Feature Distillation Network for Lightweight Image Super-Resolution}, 
      author={Jie Liu and Jie Tang and Gangshan Wu},
      year={2020},
      eprint={2009.11551},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Acknowledgments

• Any ideas on updating or misunderstanding, please send me an email: [email protected]
• If you find this repo helpful, kindly give me a star.