A Novel Super-resolution Approach Based on Supervised Canonical Correlation Analysis
- 10.2991/ccit-14.2014.76How to use a DOI?
- Super-resolution, High Resolution, Low Resolution, Supervised Canonical Correlation Analysis, Relationship Learning
In this paper, we use supervised canonical correlation analysis (SCCA) method to extract features which maximize the correlation between HR and LR face images. Then Relationship Learning (RL) is used to construct the mapping relationship between the face coherent features. SCCA method comprehensively considers the within-class information and the similarity of HR and LR images, to make the SR image closer to original HR image. Experiments on Yale and ORL face databases show that our method has higher recognition rate.
- © 2014, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Suna Xia AU - Gangmin Zheng AU - Yuanyuan Ma AU - Xiaohu Ma PY - 2014/01 DA - 2014/01 TI - A Novel Super-resolution Approach Based on Supervised Canonical Correlation Analysis BT - Proceedings of the 2014 International Conference on Computer, Communications and Information Technology PB - Atlantis Press SP - 292 EP - 295 SN - 1951-6851 UR - https://doi.org/10.2991/ccit-14.2014.76 DO - 10.2991/ccit-14.2014.76 ID - Xia2014/01 ER -