Image super-resolution representation via image patches based on extreme learning machine
Qiuxi Zhu, Xiaodong Li, Weijie Mao
Available Online September 2013.
- 10.2991/icsecs-13.2013.61How to use a DOI?
- ELM; neural network; image processing; super-resolution
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.
- © 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/).
Cite this article
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 -