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Erschienen in:
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2020 | OriginalPaper | Buchkapitel

Hyperspectral Image Super-Resolution Using Multi-scale Feature Pyramid Network

verfasst von : He Sun, Zhiwei Zhong, Deming Zhai, Xianming Liu, Junjun Jiang

Erschienen in: Digital TV and Wireless Multimedia Communication

Verlag: Springer Singapore

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Abstract

Hyperspectral (HS) images are captured with rich spectral information, which have been proved to be useful in many real-world applications, such as earth observation. Due to the limitations of HS cameras, it is difficult to obtain HS images with high-resolution (HR). Recent advances in deep learning (DL) for single image super-resolution (SISR) task provide a powerful tool for restoring high-frequency details from low-resolution (LR) input image. Inspired by this progress, in this paper, we present a novel DL-based model for single HS image super-resolution in which a feature pyramid block is designed to extract multi-scale features of the input HS image. Our method does not need auxiliary inputs which further extends the application scenes. Experiment results show that our method outperforms state-of-the-arts on both objective quality indices and subjective visual results.

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Vorheriges Kapitel Missing Elements Recovery Using Low-Rank Tensor Completion and Total Variation Minimization
Nächstes Kapitel Unsupervised Representation Learning Based on Generative Adversarial Networks
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Metadaten
Titel
Hyperspectral Image Super-Resolution Using Multi-scale Feature Pyramid Network
verfasst von
He Sun
Zhiwei Zhong
Deming Zhai
Xianming Liu
Junjun Jiang
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Digital TV and Wireless Multimedia Communication

Print ISBN: 978-981-15-3340-2

Electronic ISBN: 978-981-15-3341-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-3341-9_5

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