A tensorflow implementation of Christian et al's "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" paper. ( See : https://arxiv.org/abs/1609.04802 ) This implementation is quite different from original paper. The differences are as followings:
The existing CNN based super-resolution skill mainly use MSE loss and this makes super-resolved images look blurry. If we replace MSE loss with gradients from GAN, we may prevent the blurry artifacts of the super-resolved images and this is the key idea of this paper. I think this idea looks promising and my experiment result using MNIST data set looks good.
Execute
python train.py
to train the network. You can see the result ckpt files and log files in the 'asset/train' directory. Launch tensorboard --logdir asset/train/log to monitor training process.
Execute
python generate.py
to generate sample image. The 'sample.png' file will be generated in the 'asset/train' directory.
This image was generated by SRGAN.
Namju Kim (buriburisuri@gmail.com) at Jamonglabs Co., Ltd.