We share the super-resolved LF images generated by our LF-DFnet on all the 5 datasets for 4xSR. Then, researchers can compare their algorithms to our LF-DFnet without performing inference. Results are available at Baidu Drive (Key: nudt).

We used the EPFL, HCInew, HCIold, INRIA and STFgantry datasets for both training and test. Please first download our dataset via Baidu Drive (key:nudt) or OneDrive, then place the 5 datasets to the folder ./Datasets/.

• PyTorch 1.3.0, torchvision 0.4.1. The code is tested with python=3.7, cuda=9.0.
• Matlab (For training/test data generation and result image generation)

• Cd to code/dcn.
• For Windows users, run cmd make.bat. For Linux users, run bash bash make.sh. The scripts will build DCN automatically and create some folders. See test.py for example usage.

• Run GenerateTrainingData.m to generate training data. The generated data will be saved in ./Data/TrainData_UxSR_AxA/ (U=2,4; A=3,5,7,9).
• Run train.py to perform network training. Note that, the training settings in train.py should match the generated training data. Checkpoint will be saved to ./log/.

• Run GenerateTestData.m to generate input LFs of the test set. The generated data will be saved in ./Data/TestData_UxSR_AxA/ (U=2,4; A=3,5,7,9).
• Run test.py to perform network inference. The PSNR and SSIM values of each dataset will be printed on the screen.
• Run GenerateResultImages.m to convert '.mat' files in ./Results/ to '.png' images to ./SRimages/.

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

@article{LF-DFnet,
  author  = {Wang, Yingqian and Yang, Jungang and Wang, Longguang and Ying, Xinyi and Wu, Tianhao and An, Wei and Guo, Yulan},
  title   = {Light Field Image Super-Resolution Using Deformable Convolution},
  journal = {IEEE Transactions on Image Processing},
  volume  = {30),
  pages   = {1057-1071},
  year    = {2021},
}

The DCN part of our code is referred from DCNv2 and D3Dnet. We thank the authors for sharing their codes.

Any question regarding this work can be addressed to wangyingqian16@nudt.edu.cn.