DASR

(CVPR-2021) Official PyTorch code for our paper DASR: Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training.

Abstract

These days, unsupervised super-resolution (SR) has been soaring due to its practical and promising potential in real scenarios. The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, \ie first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs ${\mathcal{Y}^g, \mathcal{X}^r}$ for training in a supervised manner. Unfortunately, since image translation itself is an extremely challenging task, the SR performance of these approaches are severely limited by the domain gap between generated synthetic LR images and real LR images. In this paper, we propose a novel domain-distance aware super-resolution (DASR) approach for unsupervised real-world image SR. The domain gap between training data (e.g. $\mathcal{Y}^g$) and testing data (e.g. $\mathcal{Y}^r$) is addressed with our \textbf{domain-gap aware training} and \textbf{domain-distance weighted supervision} strategies. Domain-gap aware training takes additional benefit from real data in the target domain while domain-distance weighted supervision brings forward the more rational use of labeled source domain data. The proposed method is validated on synthetic and real datasets and the experimental results show that DASR consistently outperforms state-of-the-art unsupervised SR approaches in generating SR outputs with more realistic and natural textures. Code will be available at DASR.

Requirements

• Pytorch == 1.1.0
• torchvision == 0.3.0
• opencv-python
• tensorboardX

Usage

Hierarchy of DASR codes

DASR:
|
|----codes
|       |---- DSN
|       |---- SRN
|       |---- PerceptualSimilarity
|
|----DSN_experiments
|----SRN_experiments
|----DSN_tb_logger
|----SRN_tb_logger
|----DSN_results
|----SRN_results

Data Preparation

Please specify the path to your dataset in paths.yml.

We followed the AIM2019 challenge descriptions that source domain indicates the noisy and low-resolution and target domain indicates the clean and high resolution images. The images in source domain and target domain are unpaired.

Auto Reproducing

Auto reproduce process can be presented as
Training DSN --> Generating LRs and domain distance maps --> Training SRN.

Our results can be reproduced by running a single command:

cd DASR/codes
python Auto_Reproduce.py --dataset aim2019/realsr  \
                         --artifact tdsr/(tddiv2k/tdrealsr)

Auto-reproducing process will take about 48 hours on Nvidia GTX 1080.

Pretrained model

We provide pretrained models for AIM2019/ RealSR and CameraSR datasets.

Testing

Testing is similar to the BasicSR codes. We add some extra options including LPIPS,Forward_chop, etc. Please specify the pre-trained model and data path in test_sr.json correctly.

cd DASR/codes/SRN
python test.py -opt options/test/test_sr.json

DownSampling Network (DSN)

We build our DSN codes based on our previous work FSSR (github | paper).

Training

The DSN model can be trained with:

cd DASR/codes/DSN
python train.py --dataset aim2019 --artifacts tdsr \
                --generator DeResnet --discriminator FSD \
                --norm_layer Instance \
                --filter wavelet --cat_or_sum cat  \
                --batch_size 8 --num_workers 8 --crop_size 256 \
                --save_path 0603_DeResnet+wavcat+FSD+wtex0.03

Args:

• --dataset The dataset should be one of the aim2019/realsr/camerasr. To train DSN on your own data, please specify the path in the correct format in paths.yml.

• --aritifact Choose the type of degradation artifact that you specified in paths.yml.

• --generator Choose the downsampling generator architecture, including DeResnet, DSGAN.

• --discriminator Choose the discriminator architecture including nld_s1(Nlayer discriminator with stride 1), nld_s2 and FSD (Frequency separation discriminator).

• --norm_layer Choose the normalization layer in discriminator, including Instance and Batch.

• --filter Choose the frequency separation filter, including wavelet, gaussion and avg_pool.

• --cat_or_sum Choose the approach of combination of wavelet subbands, including cat and sum.

The training loss and validation results are shown in DASR/DSN_tb_logger by running with:

cd DASR/DSN_tb_logger
tensorboard --logdir=./

Generate LR-HR pairs and domain-distance maps.

The training data for SRN can be generated with:

python create_dataset_modified.py --dataset aim2019 \
                                  --checkpoint <path to your DSN model> \
                                  --generator DeResnet --discriminator FSD  --filter wavelet --cat_or_sum cat\
                                  --name 0603_DSN_LRs_aim2019

Please note that our generator and discriminator are saved in a single .tar files.

The generated low-resolution images and domain distance maps are saved in DASR/DSN_results/<--name>/.

Super-Resolution Network (SRN)

We build our SRN codes based on BasicSR super-resolution toolkit.

Training

The SRN model can be trained with:

cd DASR
python codes/train.py -opt codes/options/train/train_DASR.json

Please specify the configurations in train_DASR.json file.

Citation

@InProceedings{Wei_2021_CVPR,
    author    = {Wei, Yunxuan and Gu, Shuhang and Li, Yawei and Timofte, Radu and Jin, Longcun and Song, Hengjie},
    title     = {Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {13385-13394}
}