This repository is for TLSR introduced in the following paper
Yuanfei Huang, Jie Li, Yanting Hu, Xinbo Gao* and Wen Lu, "Transitive Learning: Exploring the Transitivity of Degradations for Blind Super-Resolution", arXiv preprint arXiv:2103.15290(2021). arXiv
- python 3.7
- pytorch >= 1.5
- NVIDIA GPU + CUDA
Download DIV2K datasets into the path "data/Datasets/Train/DIV2K".
For convolutive degradations:
- '-degrad_train' == {'type': 'B', 'min_sigma': 0.2, 'max_sigma': 2.6}
- '-degrad_test' == [{'type': 'B', 'sigma': 1.3}] # for evaluation.
For additive degradations:
- '-degrad_train' == {'type': 'N', 'min_sigma': 0, 'max_sigma': 30}
- '-degrad_test' == [{'type': 'N', 'sigma': 15}] # for evaluation.
python main.py --train 'Train'
python main.py --train 'Test'
@ARTICLE{2021arXiv210315290H,
author = {{Huang}, Yuanfei and {Li}, Jie and {Hu}, Yanting and {Gao}, Xinbo and {Lu}, Wen},
title = "{Transitive Learning: Exploring the Transitivity of Degradations for Blind Super-Resolution}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computer Vision and Pattern Recognition},
year = 2021,
month = mar,
eid = {arXiv:2103.15290},
pages = {arXiv:2103.15290},
archivePrefix = {arXiv},
eprint = {2103.15290},
primaryClass = {cs.CV},
}