Big Data Processing With Application to Image Super-Resolution

DOI

10.2991/itms-15.2015.187How to use a DOI?

Keywords

Face hallucination; Position-patch; Matrix regression

Abstract

Learning based face hallucination methods have received much attention in recent years. As opposed to the existing methods, where the input image (patch) matrix is first converted into vectors before combination coefficients calculation, this paper proposes a novel matrix based regression model for directly combination coefficients calculation to preserve the structural information of the input matrix. For each low-resolution local patch matrix, its combination coefficients over the same position image patch matrices in training images can be computed. Then the corresponding high-resolution patch matrix can be obtained. Experiments conducted on the FERET face dataset indicate that our method could outperform other state-of-the-art algorithms in terms of both vision and quantity.

Copyright

© 2015, 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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TY  - CONF
AU  - Xiangjun Meng
AU  - Baiqing Diao
AU  - Lipeng Zhu
AU  - Guangwei Gao
AU  - Song Deng
PY  - 2015/11
DA  - 2015/11
TI  - Big Data Processing With Application to Image Super-Resolution
BT  - Proceedings of the 2015 International Conference on Industrial Technology and Management Science
PB  - Atlantis Press
SP  - 791
EP  - 794
SN  - 2352-538X
UR  - https://doi.org/10.2991/itms-15.2015.187
DO  - 10.2991/itms-15.2015.187
ID  - Meng2015/11
ER  -