Alternatively Constrained Dictionary Learning for Image Superresolution

doi:10.1109/TCYB.2013.2256347

OPT OpenIR > 光谱成像技术研究室

	Alternatively Constrained Dictionary Learning for Image Superresolution
	Lu, Xiaoqiang; Yuan, Yuan; Yan, Pingkun
作者部门	光学影像学习与分析中心
	2014-03-01
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
卷号	44 期号:3 页码:366-377
摘要	Dictionaries are crucial in sparse coding-based algorithms for image superresolution. Sparse coding is a typical unsupervised learning method to study the relationship between the patches of high-and low-resolution images. However, most of the sparse coding methods for image superresolution fail to simultaneously consider the geometrical structure of the dictionary and the corresponding coefficients, which may result in noticeable superresolution reconstruction artifacts. In other words, when a low-resolution image and its corresponding high-resolution image are represented in their feature spaces, the two sets of dictionaries and the obtained coefficients have intrinsic links, which has not yet been well studied. Motivated by the development on nonlocal self-similarity and manifold learning, a novel sparse coding method is reported to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries and provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Furthermore, to utilize the model of the proposed method more effectively for single-image superresolution, this paper also proposes a novel dictionarypair learning method, which is named as two-stage dictionary training. Extensive experiments are carried out on a large set of images comparing with other popular algorithms for the same purpose, and the results clearly demonstrate the effectiveness of the proposed sparse representation model and the corresponding dictionary learning algorithm.
文章类型	Article
关键词	Image Superresolution Manifold Learning Non-local Self-similarity Two-stage Dictionary Training (Tsdt)
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2013.2256347
收录类别	SCI ; EI
关键词[WOS]	INTERPOLATION ; FEATURES
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000331906100006
引用统计	被引频次：79[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/181661/22358
专题	光谱成像技术研究室
作者单位	Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, 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. Alternatively Constrained Dictionary Learning for Image Superresolution[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014,44(3):366-377.
APA	Lu, Xiaoqiang,Yuan, Yuan,&Yan, Pingkun.(2014).Alternatively Constrained Dictionary Learning for Image Superresolution.IEEE TRANSACTIONS ON CYBERNETICS,44(3),366-377.
MLA	Lu, Xiaoqiang,et al."Alternatively Constrained Dictionary Learning for Image Superresolution".IEEE TRANSACTIONS ON CYBERNETICS 44.3(2014):366-377.

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