beantoan/DBPN-Pytorch

Deep Back-Projection Networks for Super-Resolution (Winner of NTIRE2018 and PIRM2018)

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

It contains 4 files: (1) DBPN_x2.pth, (2) DBPN_x4.pth, (3) DBPN_x8.pth are from the original architecture (CVPR2018).

(4) NTIRE2018_x8.pth is used for NTIRE2018 competition (Track 1: Classic Bicubic x8)

We also provide original Caffe implementation

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

#Training

   python3    main.py    

#Testing

   python3    eval.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}
}