%0 Conference Proceedings
%T MMSR: A Multi-model Super Resolution Framework
%+ School of Computer [Chine]
%A Yuan, Ninghui
%A Zhu, Zhihao
%A Wu, Xinzhou
%A Shen, Li
%Z Part 5: HPC
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 16th IFIP International Conference on Network and Parallel Computing (NPC)
%C Hohhot, China
%Y Xiaoxin Tang
%Y Quan Chen
%Y Pradip Bose
%Y Weiming Zheng
%Y Jean-Luc Gaudiot
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-11783
%P 197-208
%8 2019-08-23
%D 2019
%R 10.1007/978-3-030-30709-7_16
%K Super resolution
%K Multi-model
%K General framework
%K Classification
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-03770560/document
%2 https://inria.hal.science/hal-03770560/file/486810_1_En_16_Chapter.pdf
%L hal-03770560
%U https://inria.hal.science/hal-03770560
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-11783