LF-DET

This is the official pytorch implementation repository of Exploiting Spatial and Angular Correlations With Deep Efficient Transformers for Light Field Image Super-Resolution. (IEEE TMM 2023)

Datasets

Following BasicLFSR, we use five datasets, including EPFL, HCInew, HCIold, INRIA and STFgantry for training and testing. Please download the datasets in the official repository of BasicLFSR.

Besides, we use three datasets, including UrbanLF, DLFD, SLFD to validate the effectiveness of LF-DET for addressing disparity variation in LF-SSR. Please first download the datasets via Baidu Drive (key:lv31) .

Results

Code

Dependencies

• pytorch 1.8.0 + torchvision 0.9.0 + cuda 10.2 + python 3.8.10
• matlab

Training and Test Data

Please refer to BasicLFSR for detailed introduction.

Train

• Run

python train.py

• The specific configuration information is in config.py which can be changed.

Test

• Run

python test.py

• The specific configuration information is in config.py which can be changed.
• The folder pretrain contains our pre-trained models with default configuration information for 2x SR and 4x SR.

Acknowledgement

Our work and implementations are inspired and based on the following projects:

• BasicLFSR
• LFT

We sincerely thank the authors for sharing their code and amazing research work!

Citation

If you find this work helpful, please consider citing the following papers:

@article{cong2023lfdet,
  title={Exploiting Spatial and Angular Correlations With Deep Efficient Transformers for Light Field Image Super-Resolution},
  author={Cong, Ruixuan and Sheng, Hao and Yang, Da and Cui, Zhenglong and Chen, Rongshan},
  journal={IEEE Transactions on Multimedia},
  year={2023},
  publisher={IEEE}
}

Contact

If you have any questions regarding this work, please send an email to congrx@buaa.edu.cn .