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MMSR: A Multi-model Super Resolution Framework

  • Ninghui Yuan
  • Zhihao Zhu
  • Xinzhou Wu
  • Li ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)

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.

Keywords

Super resolution Multi-model General framework Classification 

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina

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