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TGSR: Real-world super-resolution as multi-task learning, NeurIPS 2023 [Paper Link]

Wenlong zhang1,2, Xiaohui Li2,3, Guangyuan Shi1, Xiangyu Chen2,4,5, Yu Qiao2,5, Xiaoyun Zhang2, Xiaoming Wu1 and Chao Dong2,5

1The HongKong Polytechnic University 2Shanghai AI Laboratory 3Shanghai Jiao Tong University
4University of Macau 5Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences

Environment

cd TGSR
pip install -r requirements.txt
python setup.py develop

How To Test

  • Refer to ./options/test for the configuration file of the model to be tested, and prepare the testing data and pretrained model.
  • The pretrained models are available at Google Drive.
  • Then run the following codes (taking RealHATGAN-TG.pth as an example):
python tgsr/test.py -opt options/test/HAT_SRx4_ImageNet-pretrain.yml

The testing results will be saved in the ./results folder.

  • Refer to ./options/test/test_Real_HAT_GAN_TG.yml for inference without the ground truth image.

Citations

BibTeX

@inproceedings{zhang2023real,
title={Real-World Image Super-Resolution as Multi-Task Learning},
author={Zhang, Wenlong and Li, Xiaohui and Guangyuan, SHI and Chen, Xiangyu and Qiao, Yu and Zhang, Xiaoyun and Wu, Xiao-Ming and Dong, Chao},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}

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