LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution (CVPR, 2021) [pdf]
Pytorch implementation for "LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution".
Python=3.7, PyTorch=1.7.0, numpy, skimage, cv2, matplotlib, tqdm
Put dataset in ./dataset.
Put pre-trained model in ./results/model.
Run the main.py:
python main.py --test_only --datastest=test --pre_train='./results/model/model_best.pt'Google Drive:
link: https://drive.google.com/drive/folders/1w7m1r-yCbbZ7_xMGzb6IBplPe4c89rH9?usp=sharing
Google Drive:
link: https://drive.google.com/drive/folders/15FxJOB0hWR3WZTg9CNxKKjGciQF8ZwSK?usp=sharing
If you find our paper or code useful for your research, please cite:
@InProceedings{Deng_2021_CVPR,
author = {Deng, Xin and Wang, Hao and Xu, Mai and Guo, Yichen and Song, Yuhang and Yang, Li},
title = {LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {9189-9198}
}
If you have any problem, please contact with me through email. I will reply soon.
My email: [email protected]