wind222 / Photo-Realistic-Super-Resoluton

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

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

train_folder=your_root_folder model_folder=your_save_folder th run_sr.lua 

<<<<<<< HEAD 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.

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.

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####Loading a saved model to train

D_path=your_saved_D_model G_path=your_saved_G_model train_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

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

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##Todo:

1. Add TV loss.
2. Provide trained models.