MR image super-resolution via manifold regularized sparse learning

doi:10.1016/j.neucom.2015.03.065

OPT OpenIR > 光谱成像技术研究室

	MR image super-resolution via manifold regularized sparse learning
	Lu, Xiaoqiang; Huang, Zihan; Yuan, Yuan
	2015-08-25
发表期刊	NEUROCOMPUTING
卷号	162 页码:96-104
摘要	Single image super-resolution (SR) has been shown useful in Magnetic Resonance (MR) image based diagnosis, where the image resolution is still limited. The basic goal of single image SR is to produce a high-resolution (HR) image from corresponding low-resolution (LR) image. However, most existing SR algorithms often fail to: (1) reflect the intrinsic structure between MR images and (2) exploit the intra-patient information of MR images. In fact, MR images are more likely to vary along a low dimensional submanifold, which can be embedded in the high dimensional space. It has also been shown that the structure information of MR images and the priors of the MR images of different modality are important for improving the image resolution. To take full advantage of manifold structure information and intra-patient prior of MR images, a novel single image super-resolution algorithm for MR images is proposed in this paper. Compared with the existing works, the proposed algorithm has the following merits: (1) the proposed sparse coding based algorithm integrates manifold constraints to handle the inverse problem in MR image SR; (2) the manifold structure of the intra-patient MR image is considered for image SR; and (3) the topological structure of the intra-patient MR image can be preserved to improve the reconstructed result. Experiments show that the proposed algorithm is more effective than the state-of-the-art algorithms. (C) 2015 Elsevier B.V. All rights reserved.
文章类型	Article
关键词	Sparse Learning Manifold Regularization Super-resolution Magnetic Resonance Imaging (Mri)
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2015.03.065
收录类别	SCI ; EI
关键词[WOS]	NONLINEAR DIMENSIONALITY REDUCTION ; RECONSTRUCTION ; RECOGNITION ; REGRESSION
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000356125200010
引用统计	被引频次：38[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/181661/25072
专题	光谱成像技术研究室
作者单位	Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Lu, Xiaoqiang,Huang, Zihan,Yuan, Yuan. MR image super-resolution via manifold regularized sparse learning[J]. NEUROCOMPUTING,2015,162:96-104.
APA	Lu, Xiaoqiang,Huang, Zihan,&Yuan, Yuan.(2015).MR image super-resolution via manifold regularized sparse learning.NEUROCOMPUTING,162,96-104.
MLA	Lu, Xiaoqiang,et al."MR image super-resolution via manifold regularized sparse learning".NEUROCOMPUTING 162(2015):96-104.

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