Tensorflow implementation of Pixel Recursive Super Resolution.
We present a pixel recursive super resolution model that
synthesizes realistic details into images while enhancing
their resolution. A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
more photo realistic than a strong L2 regression baseline.
- Python 2/3
- Numpy 1.12.0
- SkImage 0.12.3
- Tensorflow 1.0
git clone https://github.com/hodgka/Pixel-Recursive-Super-Resolution
cd Pixel-Recursive-Super-Resolution
python main.py [--options]
| option | default | description |
|---|---|---|
| dataset_dir | dataset | Path to dataset |
| model_dir | models | Output folder where models are dumped |
| image_size | 8 | Size of downsampled images in pixels |
| iterations | 2e5 | Number of iterations to train for |
| batch_size | 32 | Number of samples per batch |
| learning_rate | 4e-4 | Learning rate |
| B | 8 | Size of Resnet blocks to use |