Disparity-Guided Light Field Image Super-Resolution via Feature Modulation and Recalibration

This repository contains official pytorch implementation of Disparity-Guided Light Field Image Super-Resolution via Feature Modulation and Recalibration, an early acceped paper in IEEE transactions on Broadcasting, 2023, by Gaosheng Liu, Huanjing Yue, Kun Li, and Jingyu Yang.

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.

Code

Dependencies

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

Prepare Test Data

To generate the test data, please first download the five datasets and run:
```
GenerateTestData.m
```

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{liu2023disparity,
  title={Disparity-Guided Light Field Image Super-Resolution via Feature Modulation and Recalibration},
  author={Liu, Gaosheng and Yue, Huanjing and Li, Kun and Yang, Jingyu},
  journal={IEEE Transactions on Broadcasting},
  year={2023},
  publisher={IEEE}
}

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!

GaoshengLiu/LF-DGNet