Symmetric Parallax Attention for Stereo Image Super-Resolution, arXiv 2020.
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 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)
- Download the training sets from Baidu Drive (Key: NUDT) and unzip them to
./data/train/. - Run
./data/train/GenerateTrainingPatches.mto generate training patches. - Run
train.pyto perform training. Checkpoint will be saved to./log/.
- 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.pyto perform a demo inference. Results (.pngfiles) will be saved to./results. - Run
evaluation.mto calculate PSNR and SSIM scores.
Qualitative Results (demo video)
@artical{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},
}
Any question regarding this work can be addressed to wangyingqian16@nudt.edu.cn.