This is the PyTorch implementation of the angular SR method in our paper "Disentangling Light Fields for Super-Resolution and Disparity Estimation". Please refer to our paper and project page for details.
- PyTorch 1.3.0, torchvision 0.4.1. The code is tested with python=3.6, cuda=9.0.
- Matlab (For training/test data generation and performance evaluation)
The datasets used in our paper can be downloaded through this link.
- Run
Generate_Data_for_Training_2x2-7x7.m
to generate training data. - Run
train.py
to perform network training. - Checkpoint will be saved to
./log/
.
- Run
Generate_Data_for_Test.m
to generate test data. - Run
test.py
to perform network inference. - The PSNR and SSIM values of each dataset will be saved to
./log/
.
If you find this work helpful, please consider citing:
@Article{DistgLF,
author = {Wang, Yingqian and Wang, Longguang and Wu, Gaochang and Yang, Jungang and An, Wei and Yu, Jingyi and Guo, Yulan},
title = {Disentangling Light Fields for Super-Resolution and Disparity Estimation},
journal = {IEEE TPAMI},
year = {2022},
}
Welcome to raise issues or email to wangyingqian16@nudt.edu.cn for any question regarding this work.