Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
Switch branches/tags
Nothing to show
Clone or download
Harry Yang
Harry Yang update
Latest commit 0f3d24e Feb 13, 2017
Permalink
Failed to load latest commit information.
data update Jan 23, 2017
pics update Feb 10, 2017
.gitignore update Jan 23, 2017
LICENSE Initial commit Oct 10, 2016
README.md update Feb 12, 2017
adversarial_D.lua debug Dec 12, 2016
adversarial_G.lua debug Dec 12, 2016
run_resume.lua resolve size and scale issues Dec 12, 2016
run_sr.lua debug Dec 12, 2016
run_test.lua Update run_test.lua Nov 22, 2016
util.lua initial codebase Oct 10, 2016
weight-init.lua added test functions Oct 12, 2016

README.md

Photo-Realistic-Super-Resoluton

Torch Implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" [Paper]

This is a prototype implementation developed by Harry Yang.

Getting started

####Training prepare your images under a sub-folder of a root folder

t_folder=your_root_folder model_folder=your_save_folder/ th run_sr.lua 

By default it resizes the images to 96x96 as ground truth and 24x24 as input, but you can specify the size using loadSize. Note current generator network only supports 4x super-resolution. In addition, the input size must be dividable by 32 (such as 96, 128, 160, etc.).

By default it resizes the images to 96x96 as ground truth and 24x24 as input, but you can specify the size using loadSize and scale.

####Loading a saved model to train

D_path=your_saved_D_model G_path=your_saved_G_model t_folder=your_root_folder model_folder=your_save_folder/ th run_resume.lua

####Testing prepare your test images under a sub-folder of a root folder

t_folder=your_root_folder model_file=your_G_model result_path=location_to_save_results th run_test.lua

Report Issues

Contact

Citation

If you find this code useful for your research, please cite:

@misc{Johnson2015,
  author = {Yang, Harry},
  title = {super-resolution using GAN},
  year = {2016},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/leehomyc/Photo-Realistic-Super-Resoluton}},
}