A Novel Super-resolution Approach Based on Supervised Canonical Correlation Analysis

DOI

10.2991/ccit-14.2014.76How to use a DOI?

Keywords

Super-resolution, High Resolution, Low Resolution, Supervised Canonical Correlation Analysis, Relationship Learning

Abstract

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.

Copyright

© 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/).

Download article (PDF)

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  -