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README.md

Symmetric Parallax Attention for Stereo Image Super-Resolution, arXiv 2020. [pdf], [demo].

Yingqian Wang   Xinyi Ying   Longguang Wang   Jungang Yang   Wei An   Yulan Guo


Download the Results

We share the quantitative and qualitative results achieved by our iPASSR on all the test sets for both 2xSR and 4xSR. Then, researchers can compare their algorithms to our method without performing inference. Results are available at Baidu Drive (Key: NUDT).

PyTorch Implementation

Requirement

  • 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 performance evaluation)

Train

  • Download the training sets from Baidu Drive (Key: NUDT) and unzip them to ./data/train/.
  • Run ./data/train/GenerateTrainingPatches.m to generate training patches.
  • Run train.py to perform training. Checkpoint will be saved to ./log/.

Test

  • Download the test sets and unzip them to ./data. Here, we provide the full test sets used in our paper on Baidu Drive (Key: NUDT).
  • Run test.py to perform a demo inference. Results (.png files) will be saved to ./results.
  • Run evaluation.m to calculate PSNR and SSIM scores.

Citiation

@article{iPASSR,
  author    = {Wang, Yingqian and Ying, Xinyi and Wang, Longguang and Yang, Jungang and An, Wei and Guo, Yulan},
  title     = {Symmetric Parallax Attention for Stereo Image Super-Resolution},
  journal   = {arXiv Preprint: 2011.03802},
  year      = {2020},
}

Contact

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

About

Symmetric Parallax Attention for Stereo Image Super-Resolution, arXiv 2020.

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