Intra-Inter View Interaction Network for Light Field Image Super-Resolution

This repository contains official pytorch implementation of Intra-Inter View Interaction Network for Light Field Image Super-Resolution in TMM 2021, by Gaosheng Liu, Huanjing Yue, Jiamin Wu, and Jingyu Yang. TMM 2021 LF-IINet

Dataset

We use the processed data by LF-DFnet, including EPFL, HCInew, HCIold, INRIA and STFgantry datasets for training and testing. Please download the dataset in the official repository of LF-DFnet.

Results

We share the SR LF images generated by our LF-IINet on all the 5 datasets for 2x and 4x SR, which are avaliable at Baidu Drive (key:8tbv).

Code

Dependencies

• Ubuntu 18.04
• Python 3.6
• Pyorch 1.3.1 + torchvision 0.4.2 + cuda 92
• Matlab

Prepare Training and Test Data

• To generate the training data, please first download the five datasets and run:

  GenerateTrainingData.m

• To generate the test data, run:

  GenerateTestData.m

Train

• Run:

  python train.py

Test

• Run:

  python test.py

Visual Results

• To merge the Y, Cb, Cr channels, run:

  GenerateResultImages.m

Citation

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

@article{liu2021intra,
  title={Intra-Inter View Interaction Network for Light Field Image Super-Resolution},
  author={Liu, Gaosheng and Yue, Huanjing and Wu, Jiamin and Yang, Jingyu},
  journal={IEEE Transactions on Multimedia},
  year={2021},
  publisher={IEEE}
}

@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},
}

Acknowledgement

Our work and implementations are inspired and based on the following projects:
LF-DFnet
LF-InterNet
We sincerely thank the authors for sharing their code and amazing research work!