RankSRGAN

Paper | Supplementary file | Project Page

RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution

By Wenlong Zhang, Yihao Liu, Chao Dong, Yu Qiao

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Dependencies

• Python 3 (Recommend to use Anaconda)
• PyTorch >= 1.0.0
• NVIDIA GPU + CUDA
• Python packages: pip install numpy opencv-python lmdb
• [option] Python packages: pip install tensorboardX, for visualizing curves.

Codes

• We update the codes version based on mmsr. The old version can be downloaded from Google Drive
• 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. 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.yml

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 ./datasets/generate_rankdataset/
3. Run command:

python train_rank.py -opt options/train/train_Ranker.yml

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 mmsr_SRResNet_pretrain.pth as the pretrained model that can be downloaded from Google Drive.
3. Modify the configuration file options/train/train_RankSRGAN.json
4. Run command:

python train.py -opt options/train/train_RankSRGAN.yml

or

python train_niqe.py -opt options/train/train_RankSRGAN.yml

Using the train.py can output the convergence curves with PSNR; Using the train_niqe.py can output the convergence curves with NIQE and PSNR.

Acknowledgement

• Part of this codes was done by Yihao Liu.
• This codes are based on BasicSR.