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	Recursive Inception Network for Super-Resolution
	Tao Jiang; Yu Zhang; W. Shui; Gang Lu; Xiaojun Wu; S. Guo; Fei Hao; Qieshi Zhang
	2018
会议日期	2018
英文摘要	Abstract—In this paper, we propose a novel network for superresolution and achieve the state-of-the-art performance with limited parameters. Inspired by the previous methods, we use ResNet to learn the residual part of the input patches. In addition, we introduce an inception-like structure that helps to extract features and a weight sharing mechanism is utilized among these inception blocks. By cascading multi-scale filters with separate paths in a deep network, the proposed method can fully exploit the contextual information over large image regions. Besides, the residual learning module makes the training phase easy to converge. Extensive experiments demonstrate that the proposed method can achieve the same performance with fewer parameters compared with the previous state-of-the-art methods.
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/13778]
专题	深圳先进技术研究院_集成所
推荐引用方式 GB/T 7714	Tao Jiang,Yu Zhang,W. Shui,et al. Recursive Inception Network for Super-Resolution[C]. 见:. 2018.

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