Video super-resolution with 3D adaptive normalized convolution

doi:10.1016/j.neucom.2012.03.012

OPT OpenIR > 光谱成像技术研究室

	Video super-resolution with 3D adaptive normalized convolution
	Zhang, Kaibing 2; Mu, Guangwu 2; Yuan, Yuan1 ; Gao, Xinbo 2; Tao, Dacheng 3,4
作者部门	光学影像分析与学习中心
	2012-10-01
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	94 页码:140-151
产权排序	2
摘要	The classic multi-image-based super-resolution (SR) methods typically take global motion pattern to produce one or multiple high-resolution (HR) versions from a set of low-resolution (LR) images. However, due to the influence of aliasing and noise, it is difficult to obtain highly accurate registration with sub-pixel accuracy. Moreover, in practical applications, the global motion pattern is rarely found in the real LR inputs. In this paper, to surmount or at least reduce the aforementioned problems, we develop a novel SR framework for video sequence by extending the traditional 2-dimentional (2D) normalized convolution (NC) to 3-dimentional (3D) case. In the proposed framework, to bypass explicit motion estimation, we estimate a target pixel by taking a weighted average of pixels from its neighborhood. We further up-scale the input video sequence in temporal dimension based on the extended 3D NC and hence more video frames can be generated. Fundamental experiments demonstrate the effectiveness of the proposed SR framework both quantitatively and perceptually. (C) 2012 Elsevier B.V. All rights reserved.
文章类型	Article
关键词	Normalized Convolution (Nc) Motion Estimation Video Super-resolution (Sr)
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2012.03.012
收录类别	SCI ; EI
关键词[WOS]	HIGH-RESOLUTION IMAGE ; RECONSTRUCTION ALGORITHM ; MOTION ESTIMATION ; REGRESSION
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000307087000014
引用统计	被引频次：17[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/181661/20265
专题	光谱成像技术研究室
作者单位	1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China 2.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 4.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia
推荐引用方式 GB/T 7714	Zhang, Kaibing,Mu, Guangwu,Yuan, Yuan,et al. Video super-resolution with 3D adaptive normalized convolution[J]. NEUROCOMPUTING,2012,94:140-151.
APA	Zhang, Kaibing,Mu, Guangwu,Yuan, Yuan,Gao, Xinbo,&Tao, Dacheng.(2012).Video super-resolution with 3D adaptive normalized convolution.NEUROCOMPUTING,94,140-151.
MLA	Zhang, Kaibing,et al."Video super-resolution with 3D adaptive normalized convolution".NEUROCOMPUTING 94(2012):140-151.

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