A Joint Perspective Towards Image Super-Resolution: Unifying External- and Self-Examples
Abstract
Existing example-based super resolution (SR) methods are built upon either external-examples or self-examples. Although effective in certain cases, both methods suffer from their inherent limitation. This paper goes beyond these two classes of most common example-based SR approaches, and proposes a novel joint SR perspective. The joint SR exploits and maximizes the complementary advantages of external- and self-example based methods. We elaborate on exploitable priors for image components of different nature, and formulate their corresponding loss functions mathematically. Equipped with that, we construct a unified SR formulation, and propose an iterative joint super resolution (IJSR) algorithm to solve the optimization. Such a joint perspective approach leads to an impressive improvement of SR results both quantitatively and qualitatively.
Cite
Text
Wang et al. "A Joint Perspective Towards Image Super-Resolution: Unifying External- and Self-Examples." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836048Markdown
[Wang et al. "A Joint Perspective Towards Image Super-Resolution: Unifying External- and Self-Examples." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/wang2014wacv-joint/) doi:10.1109/WACV.2014.6836048BibTeX
@inproceedings{wang2014wacv-joint,
title = {{A Joint Perspective Towards Image Super-Resolution: Unifying External- and Self-Examples}},
author = {Wang, Zhangyang and Wang, Zhaowen and Chang, Shiyu and Yang, Jianchao and Huang, Thomas S.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {596-603},
doi = {10.1109/WACV.2014.6836048},
url = {https://mlanthology.org/wacv/2014/wang2014wacv-joint/}
}