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

Wenlong zhang^1,2, Xiaohui Li^2,3, Guangyuan Shi¹, Xiangyu Chen^2,4,5, Yu Qiao^2,5, Xiaoyun Zhang², Xiaoming Wu¹ and Chao Dong^2,5

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

Environment

• PyTorch >= 1.7
• BasicSR == 1.3.4.9

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}
}