RankSRGAN

ICCV 2019 (oral) RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution.

Dependencies

• Python 3 (Recommend to use Anaconda)
• PyTorch >= 0.4.0
• NVIDIA GPU + CUDA
• Python packages: pip install numpy opencv-python lmdb
• [option] Python packages: pip install tensorboardX, for visualizing curves.

Codes

This version is under testing. We will provide more details of RankSRGAN in near future.

How to Test

1. Clone this github repo.

git clone https://github.com/WenlongZhang0724/RankSRGAN.git
cd RankSRGAN

2. Place your own low-resolution images in ./LR folder.
3. Download pretrained models from Google Drive or Baidu Drive. Place the models in ./experiments/pretrained_models/. We provide three Ranker models and three RankSRGAN models (see model list).
4. Run test. We provide RankSRGAN (NIQE, Ma, PI) model and you can config in the test.py.

python test.py -opt options/test/test_ranksrgan.json

5. The results are in ./results folder.

How to Train

Train Ranker

1. Download DIV2K and Flickr2K from Google Drive or Baidu Drive
2. Generate rank dataset ./datascripts/generate_rankdataset/
3. Run command:

python train.py -opt options/train/Ranker.json

Train RankSRGAN

We use a PSNR-oriented pretrained SR model to initialize the parameters for better quality.

1. Prepare datasets, usually the DIV2K dataset.
2. Prerapre the PSNR-oriented pretrained model. You can use the SRResNet_bicx4_in3nf64nb16.pth as the pretrained model that can be downloaded from Google Drive or Baidu Drive.
3. Modify the configuration file options/train/RankSRGAN_NIQE.json
4. Run command:

python train.py -opt options/train/RankSRGAN_NIQE.json

or

python train_PI.py -opt options/train/RankSRGAN_NIQE.json

Using the train.py can output the convergence curves with NIQE and PSNR; Using the train_PI.py can output the convergence curves with NIQE, Ma, PI and PSNR.

GitHub