szw811/DBPN-Pytorch

The project is an official implement of our CVPR2018 paper "Deep Back-Projection Networks for Super-Resolution" (Winner of NTIRE2018 and PIRM2018)

NEWS

• Jan 10, 2019 -> Added model used for PIRM2018, and support Pytorch >= 1.0.0
• Mar 25, 2019 -> NEW paper on Video Super-Resolution RBPN (to appear in CVPR2019)
• Apr 12, 2019 -> Added Extension of DBPN paper and model.

Deep Back-Projection Networks for Super-Resolution (CVPR2018)

Winner (1st) of NTIRE2018 Competition (Track: x8 Bicubic Downsampling)

Winner of PIRM2018 (1st on Region 2, 3rd on Region 1, and 5th on Region 3)

Project page: https://alterzero.github.io/projects/DBPN.html

We also provide original Caffe implementation

Pretrained models and Results

Pretrained models (DBPNLL) and results can be downloaded from this link! https://drive.google.com/drive/folders/1ahbeoEHkjxoo4NV1wReOmpoRWbl448z-?usp=sharing

Dependencies

• Python 3.5
• PyTorch >= 1.0.0

Model types

1. "DBPN" -> use T = 7
2. "DBPNLL" -> use T = 10
3. PIRM Model -> "DBPNLL" with adversarial loss
4. "DBPN-RES-MR64-3" -> improvement of DBPN with recurrent process + residual learning

##########HOW TO##########

#Training

   python3    main.py    

#Testing

   python3    eval.py    

#Training GAN for PIRM2018

   python3    main_gan.py    

#Testing GAN for PIRM2018

   python3    eval_gan.py    

Citations

If you find this work useful, please consider citing it.

@inproceedings{DBPN2018,
  title={Deep Back-Projection Networks for Super-Resolution},
  author={Haris, Muhammad and Shakhnarovich, Greg and Ukita, Norimichi},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
}

@article{DBPN2019,
  title={Deep Back-Projection Networks for Single Imaage Super-Resolution},
  author={Haris, Muhammad and Shakhnarovich, Greg and Ukita, Norimichi},
  journal={arXiv preprint arXiv:1904.05677},
  year={2019}
}