Official pytorch codes for the paper:
The code and dataset will be released here once our paper is accepcted.
We conduct experiments on three challenging image restoration tasks: image reflection removal, image deraining, and image dehazing.
The overall framework of our proposed GLSGN.This dataset includes three parts: UHR4K-Syn, UHR4K-Real, and UHR4k-Rain.
If you find this work useful for your research, please cite:
@article{feng2022global,
title={Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration},
author={Feng, Xin and Ji, Haobo and Pei, Wenjie, and Lu, Guangming},
journal={arXiv preprint arXiv:2207.08808},
year={2022}
}