Proceedings of the 2014 International Conference on Computer, Communications and Information Technology

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

Authors
Suna Xia, Gangmin Zheng, Yuanyuan Ma, Xiaohu Ma
Corresponding Author
Suna Xia
Available Online January 2014.
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/).

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Volume Title
Proceedings of the 2014 International Conference on Computer, Communications and Information Technology
Series
Advances in Intelligent Systems Research
Publication Date
January 2014
ISBN
978-90786-77-97-0
ISSN
1951-6851
DOI
10.2991/ccit-14.2014.76How to use a DOI?
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/).

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  -