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Erschienen in:
Large-Scale Scene Recognition Based on Extreme Learning Machines Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Image Super-Resolution by PSOSEN of Local Receptive Fields Based Extreme Learning Machine

verfasst von : Yan Song, Bo He, Yue Shen, Rui Nian, Tianhong Yan

Erschienen in: Proceedings of ELM-2015 Volume 2

Verlag: Springer International Publishing

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Abstract

Image super-resolution aims at generating high-resolution images from low-resolution inputs. In this paper, we propose a novel learning-based and efficient image super-resolution approach called particle swarm optimization based selective ensemble (PSOSEN) of local receptive fields based extreme learning machine (ELM-LRF). ELM-LRF is locally connected ELM, which can directly process information including strong correlations such as images. PSOSEN is a selective ensemble used to optimize the output of ELM-LRF. This method constructs an end-to-end mapping of which the input is a single low-resolution image and the output is a high resolution image. Experiments show that our method is better in terms of accuracy and speed with different magnification factors compared to the state-of-the-art methods.

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Vorheriges Kapitel A High Speed Multi-label Classifier Based on Extreme Learning Machines
Nächstes Kapitel Sparse Extreme Learning Machine for Regression
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Metadaten
Titel
Image Super-Resolution by PSOSEN of Local Receptive Fields Based Extreme Learning Machine
verfasst von
Yan Song
Bo He
Yue Shen
Rui Nian
Tianhong Yan
Copyright-Jahr
2016
Buch
Proceedings of ELM-2015 Volume 2

Print ISBN: 978-3-319-28372-2

Electronic ISBN: 978-3-319-28373-9

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-28373-9_38