Tensorflow implementation of LapSRN algorithm described in [1]. It can now support training for 2x, 4x, and 8x scaling factor.

To run the training:

1. Download training dataset (DIV2K [2] [3])
   bash download_trainds.sh
2. Run the training for 4X scaling factor
   python main.py --train --scale 4
   or
   Set training images directory
   python main.py --train --scale 4 --traindir /path/to/dir

To run the test:
python3 main.py --test --scale 4
python3 main.py --test --scale 4 --testimg /path/to/image

To export file to .pb format:

1. Run the export script
   python3 main.py --export --scale 4

References

[1] Lai, W., Huang, J., Ahuja, N. and Yang, M. (2019). Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks. Available at: https://arxiv.org/abs/1710.01992
[2] Agustsson, E., Timofte, R. (2017). NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study. Available at: https://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf
https://data.vision.ee.ethz.ch/cvl/DIV2K/