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2018 | OriginalPaper | Chapter

Deep Learning Based Single Image Super-Resolution: A Survey

Authors : Viet Khanh Ha, Jinchang Ren, Xinying Xu, Sophia Zhao, Gang Xie, Valentin Masero Vargas

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

Image super-resolution is a process of obtaining one or more high-resolution image from single or multiple samples of low-resolution images. Due to its wide applications, a number of different techniques have been developed recently, including interpolation-based, reconstruction-based and learning-based. The learning-based methods have recently attracted increasing great attention due to their capability in predicting the high-frequency details lost in low resolution image. This survey mainly provides an overview on most of published work for single image reconstruction using Convolutional Neural Network. Furthermore, common issues in super-resolution algorithms, such as imaging models, improvement factor and assessment criteria are also discussed.

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Title: Deep Learning Based Single Image Super-Resolution: A Survey
Authors: Viet Khanh Ha
Jinchang Ren
Xinying Xu
Sophia Zhao
Gang Xie
Valentin Masero Vargas
Publisher: Springer International Publishing
Book: Advances in Brain Inspired Cognitive Systems
Print ISBN: 978-3-030-00562-7

Electronic ISBN: 978-3-030-00563-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00563-4_11

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