Image super-resolution representation via image patches based on extreme learning machine

DOI

10.2991/icsecs-13.2013.61How to use a DOI?

Keywords

ELM; neural network; image processing; super-resolution

Abstract

In this paper, aimed at the extensively existing problem of slowness in mainstream image super-resolutions, an efficient approach is proposed for super-resolution based on the extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs). Features and issues (e.g. parameter selections) in the application of ELM are discussed, on the basis of which a general framework for a variety of super-resolution problems is proposed, and corresponding experiments are conducted. It is shown in the results that the proposed approach can achieve relatively good quality and much faster speed compared to traditional reconstruction-based super-resolutions, therefore the effectiveness of this method is demonstrated.

Copyright

© 2013, 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  - Qiuxi Zhu
AU  - Xiaodong Li
AU  - Weijie Mao
PY  - 2013/09
DA  - 2013/09
TI  - Image super-resolution representation via image patches based on extreme learning machine
BT  - Proceedings of the 2013 International Conference on Software Engineering and Computer Science
PB  - Atlantis Press
SP  - 277
EP  - 282
SN  - 1951-6851
UR  - https://doi.org/10.2991/icsecs-13.2013.61
DO  - 10.2991/icsecs-13.2013.61
ID  - Zhu2013/09
ER  -