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Transitive Learning: Exploring the Transitivity of Degradations for Blind Super-Resolution (TLSR)

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

Dependenices

  • python 3.7
  • pytorch >= 1.5
  • NVIDIA GPU + CUDA

Models

Data preparing

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.

Train

python main.py --train 'Train'

Test

python main.py --train 'Test'

Citation

@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},
}

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