Proceedings of the 2015 International Conference on Industrial Technology and Management Science

Big Data Processing With Application to Image Super-Resolution

Authors
Xiangjun Meng, Baiqing Diao, Lipeng Zhu, Guangwei Gao, Song Deng
Corresponding Author
Xiangjun Meng
Available Online November 2015.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 International Conference on Industrial Technology and Management Science
Part of series
Advances in Computer Science Research
Publication Date
November 2015
ISBN
978-94-6252-123-0
ISSN
2352-538X
DOI
https://doi.org/10.2991/itms-15.2015.187How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2015 International Conference on Industrial Technology and Management Science
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/itms-15.2015.187
DO  - https://doi.org/10.2991/itms-15.2015.187
ID  - Meng2015/11
ER  -