A Unified Learning Framework for Single Image Super-Resolution

doi:10.1109/TNNLS.2013.2281313

OPT OpenIR > 光谱成像技术研究室

	A Unified Learning Framework for Single Image Super-Resolution
	Yu, Jifei 1; Gao, Xinbo 1; Tao, Dacheng2,3 ; Li, Xuelong4 ; Zhang, Kaibing 5
作者部门	光学影像学习与分析中心
	2014-04-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	25 期号:4 页码:780-792
摘要	It has been widely acknowledged that learning- and reconstruction-based super-resolution (SR) methods are effective to generate a high-resolution (HR) image from a single low-resolution (LR) input. However, learning-based methods are prone to introduce unexpected details into resultant HR images. Although reconstruction-based methods do not generate obvious artifacts, they tend to blur fine details and end up with unnatural results. In this paper, we propose a new SR framework that seamlessly integrates learning-and reconstruction-based methods for single image SR to: 1) avoid unexpected artifacts introduced by learning-based SR and 2) restore the missing high-frequency details smoothed by reconstruction-based SR. This integrated framework learns a single dictionary from the LR input instead of from external images to hallucinate details, embeds nonlocal means filter in the reconstruction-based SR to enhance edges and suppress artifacts, and gradually magnifies the LR input to the desired high-quality SR result. We demonstrate both visually and quantitatively that the proposed framework produces better results than previous methods from the literature.
文章类型	Article
关键词	Example Learning-based Image Super-resolution (Sr) Reconstruction-based Self-similarity
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TNNLS.2013.2281313
收录类别	SCI ; EI
关键词[WOS]	SUPPORT VECTOR REGRESSION ; HIGH-RESOLUTION IMAGE ; QUALITY ASSESSMENT ; INTERPOLATION ; ALGORITHMS ; RECOVERY ; LIMITS
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000333098700011
引用统计	被引频次：77[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/22383
专题	光谱成像技术研究室
作者单位	1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 5.Hubei Engn Univ, Sch Comp & Informat Sci, Xiaogan 432000, Peoples R China
推荐引用方式 GB/T 7714	Yu, Jifei,Gao, Xinbo,Tao, Dacheng,et al. A Unified Learning Framework for Single Image Super-Resolution[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2014,25(4):780-792.
APA	Yu, Jifei,Gao, Xinbo,Tao, Dacheng,Li, Xuelong,&Zhang, Kaibing.(2014).A Unified Learning Framework for Single Image Super-Resolution.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,25(4),780-792.
MLA	Yu, Jifei,et al."A Unified Learning Framework for Single Image Super-Resolution".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 25.4(2014):780-792.

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A Unified Learning F（3831KB）	期刊论文	出版稿	限制开放	CC BY	请求全文