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DADA

Pytorch code of CVPR-2022 paper: Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution.

fig1

fig2

Requirements

Usage

Data Preparation

Download the DRealSR dataset and set the corresponding dataset path in constant.py.

Image files for different cameras are listed in ./image_list/train.csv and ./image_list/test.csv (for x4 scale factor).

Training

  1. Train the up-sampling model:

    cd script
    sh train_up.sh
    
  2. Train up-sampling model and down-sampling model collabotively:

    cd script
    sh train_circle.sh
    
  3. Train DADA:

    cd script
    sh train_adapt.sh
    

Testing

cd script
sh test_adapt.sh

Pretrained models

Pretrianed models will be uploaded soon.

Citiation

@article{xu2022dual,
  title={Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution},
  author={Xu, Xiaoqian and Wei, Pengxu and Chen, Weikai and Mao, Mingzhi and Lin, Liang and Li, Guanbin},
  journal={arXiv preprint arXiv:2205.03524},
  year={2022}
}