YingqianWang / DistgASR

[TPAMI 2022] DistgASR: Disentangling Mechanism for Light Field Angular Super-Resolution

DistgASR: Disentangling Mechanism for Light Field Angular Super-Resolution

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.

Network Architecture:

Codes and Models:

Requirement:

• 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)

Datasets:

The datasets used in our paper can be downloaded through this link.

Train:

• 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/.

Test:

• 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/.
  
  

Results:

Quantitative Results:

Visual Comparisons:

Angular Consistency:

Citiation

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},   
}

Contact

Welcome to raise issues or email to [e-mail] for any question regarding this work.