Image Super-Resolution via Double Sparsity Regularized Manifold Learning

doi:10.1109/TCSVT.2013.2244798

OPT OpenIR > 光谱成像技术研究室

	Image Super-Resolution via Double Sparsity Regularized Manifold Learning
	Lu, Xiaoqiang; Yuan, Yuan; Yan, Pingkun
	2013-12-01
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷号	23 期号:12 页码:2022-2033
摘要	Over the past few years, high resolutions have been desirable or essential, e. g., in online video systems, and therefore, much has been done to achieve an image of higher resolution from the corresponding low-resolution ones. This procedure of recovering/rebuilding is called single-image super-resolution (SR). Performance of image SR has been significantly improved via methods of sparse coding. That is to say, the image frame patch can be sparse linear combinations of basis elements. However, most of these existing methods fail to consider the local geometrical structure in the space of the training data. To take this crucial issue into account, this paper proposes a method named double sparsity regularized manifold learning (DSRML). DSRML can preserve the properties of the aforementioned local geometrical structure by employing manifold learning, e. g., locally linear embedding. Based on a large amount of experimental results, DSRML is demonstrated to be more robust and more effective than previous efforts in the task of single-image SR.
文章类型	Article
关键词	Double Sparsity Manifold Learning Single-image Super-resolution (Sr) Sparse Coding
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCSVT.2013.2244798
收录类别	SCI ; EI
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION ; QUALITY ASSESSMENT ; ALGORITHM ; REPRESENTATIONS ; RECONSTRUCTION ; INTERPOLATION ; DICTIONARIES ; INFORMATION ; REGRESSION ; ROBUST
语种	英语
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000328047000002
引用统计	被引频次：80[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/181661/23467
专题	光谱成像技术研究室
作者单位	Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Lu, Xiaoqiang,Yuan, Yuan,Yan, Pingkun. Image Super-Resolution via Double Sparsity Regularized Manifold Learning[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2013,23(12):2022-2033.
APA	Lu, Xiaoqiang,Yuan, Yuan,&Yan, Pingkun.(2013).Image Super-Resolution via Double Sparsity Regularized Manifold Learning.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,23(12),2022-2033.
MLA	Lu, Xiaoqiang,et al."Image Super-Resolution via Double Sparsity Regularized Manifold Learning".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 23.12(2013):2022-2033.

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文件名称/大小	文献类型	版本类型	开放类型	使用许可
Image Super-Resoluti（1039KB）	期刊论文	出版稿	限制开放	CC BY	请求全文