/SGH

Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment

Primary LanguagePython

Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment

Paper

visitors

TODO

  • Code release
  • Upload datasets

Introduction

  • log: save training log
  • nets: define iqa model
  • save: save model
  • nets: define iqa model
  • datasets.py: define datasets
  • datasets_deepsrq.py: define datasets for deepsrq
  • engine.py: training and test engine
  • train_test_IQA.py: setup training and test

Train and Test

python train_test_IQA.py --dataset xxx --netFile xxx --gpuid x --batch_size 64

Some mandatory options:

  • --dataset: string, Training and testing dataset, support datasets: 'CVIU' | 'QADS'| 'Waterloo'.
  • --netFile: string, IQA model, support models: 'DBCNN' | 'HyperIQA' | 'CNNIQA' | 'Resnet50' | 'JCSAN' | 'DeepSRQ'.
  • --gpuid: int, gpu device
  • --batch_size: int, Batch size, 64.

Acknowledgement

This project is based on HyperIQA. Thanks for the awesome work.

Citation

Please cite the following paper if you use this repository in your reseach.

@article{fu2023scale,
  title={Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment},
  author={Fu, Jun},
  journal={arXiv preprint arXiv:2306.02398},
  year={2023}
}

Contact

For any questions, feel free to contact: fujun@mail.ustc.edu.cn