MAANet: Multi-view Aware Attention Networks for Image Super-Resolution implementation in Pytorch.
Deep Convolutional Neural Networks (DCNNs) based Image Super-Resolution (SR) with Local and Global Aware Attention modules.
More details can be found in the the paper.
- Pytorch 1.7 +
- Torchvision 0.8.2 +
- Numpy 1.14 +
- Scikit-Image 0.16 +
- Train model for 106 Epochs.
DIV2K: DIVerse 2K resolution high quality images.
Dataset can be downloaded from this link.
@InProceedings{Agustsson_2017_CVPR_Workshops,
author = {Agustsson, Eirikur and Timofte, Radu},
title = {NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}
}
Original Paper and Architecture details can be found in the url below.
@article{DBLP:journals/corr/abs-1904-06252,
author = {Jingcai Guo and
Shiheng Ma and
Song Guo},
title = {MAANet: Multi-view Aware Attention Networks for Image Super-Resolution},
journal = {CoRR},
volume = {abs/1904.06252},
year = {2019},
url = {http://arxiv.org/abs/1904.06252},
archivePrefix = {arXiv},
eprint = {1904.06252},
timestamp = {Sat, 23 Jan 2021 01:19:38 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1904-06252.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}