trevor-m / Tensorflow Srgan
Licence: mit
Tensorflow implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" (Ledig et al. 2017)
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SRGan in Tensorflow
This is an implementation of the paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network using TensorFlow.
Usage
Set up
- Download the VGG19 weights provided by TensorFlow-Slim. Place the vgg_19.ckpt file in this directory.
- Download a dataset of images. I recommend ImageNet or Places205. Specify the directory containing your dataset using the
--train-dirargument when training the model.
Training
SRResNet-MSE
python train.py --name srresnet-mse --content-loss mse --train-dir path/to/dataset
SRResNet-VGG22
python train.py --name srresnet-vgg22 --content-loss vgg22 --train-dir path/to/dataset
SRGAN-MSE
python train.py --name srgan-mse --use-gan --content-loss mse --train-dir path/to/dataset --load results/srresnet-mse/weights-1000000
SRGAN-VGG22
python train.py --name srgan-vgg22 --use-gan --content-loss vgg22 --train-dir path/to/dataset --load results/srresnet-mse/weights-1000000
SRGAN-VGG54
python train.py --name srgan-vgg54 --use-gan --content-loss vgg54 --train-dir path/to/dataset --load results/srresnet-mse/weights-1000000
Results
| Set5 | Ledig SRResNet | This SRResNet | Ledig SRGAN | This SRGAN |
|---|---|---|---|---|
| PSNR | 32.05 | 32.11 | 29.40 | 28.21 |
| SSIM | 0.9019 | 0.8933 | 0.8472 | 0.8200 |
| Set14 | Ledig SRResNet | This SRResNet | Ledig SRGAN | This SRGAN |
|---|---|---|---|---|
| PSNR | 28.49 | 28.61 | 26.02 | 25.74 |
| SSIM | 0.8184 | 0.7809 | 0.7397 | 0.6909 |
| BSD100 | Ledig SRResNet | This SRResNet | Ledig SRGAN | This SRGAN |
|---|---|---|---|---|
| PSNR | 27.58 | 27.57 | 25.16 | 24.80 |
| SSIM | 0.7620 | 0.7346 | 0.6688 | 0.6314 |
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