Update log

• Jan 10, 2019 -> Added model used for PIRM2018, and support Pytorch >= 1.0.0

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: http://www.toyota-ti.ac.jp/Lab/Denshi/iim/members/muhammad.haris/projects/DBPN.html

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

Dependencies

• Python 3.5
• PyTorch >= 1.0.0

We also provide original Caffe implementation

##########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}
}