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2019 | OriginalPaper | Chapter

MMSR: A Multi-model Super Resolution Framework

Authors : Ninghui Yuan, Zhihao Zhu, Xinzhou Wu, Li Shen

Published in: Network and Parallel Computing

Publisher: Springer International Publishing

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Abstract

Single image super-resolution (SISR), as an important image processing method, has received great attentions from both industry and academia. Currently, most super-resolution image reconstruction approaches are based on the deep-learning techniques and they usually focus on the design and optimization of different network models. But they usually ignore the differences among image texture features and use the same model to train all the input images, which greatly influence the training efficiency. In this paper, we try to build a framework to improve the training efficiency through specifying an appropriate model for each type of images according to their texture characteristics, and we propose MMSR, a multi-model super resolution framework. In this framework, all input images are classified by an approach called TVAT (Total Variance above the Threshold). Experimental results indicate that our MMSR framework brings a 66.7% performance speedup on average without influencing the accuracy of the results HR images. Moreover, MMSR framework exhibits good scalability.

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To distinguish between the output images and the reference images, the output images are called SR (Super-Resolution) images and the reference images are called HR (High-Resolution) images in this paper.

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Title: MMSR: A Multi-model Super Resolution Framework
Authors: Ninghui Yuan
Zhihao Zhu
Xinzhou Wu
Li Shen
Publisher: Springer International Publishing
Book: Network and Parallel Computing
Print ISBN: 978-3-030-30708-0

Electronic ISBN: 978-3-030-30709-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-30709-7_16

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