Sparse coding for image denoising using spike and slab prior

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Sparse coding for image denoising using spike and slab prior
	Lu, Xiaoqiang; Yuan, Yuan; Yan, Pingkun
刊名	neurocomputing
	2013-04-15
卷号	106 页码:12-20
关键词	Image denoising Sparse representation Spike and slab prior
英文摘要	sparse coding is a challenging and promising theme in image denoising. its main goal is to learn a sparse representation from an over-complete dictionary. how to obtain a better sparse representation from the dictionary is important for the denoising process. in this paper, starting from the classic image denoising problem, a bayesian-based sparse coding algorithm is proposed, which learns sparse representation with the spike and slab prior. using the spike and slab prior, the proposed algorithm can achieve accurate prediction performance and effectively enforce sparsity. experimental results on image denoising have demonstrated that the proposed algorithm can provide better representation and obtain excellent denoising performance. (c) 2012 elsevier b.v. all rights reserved.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence
研究领域[WOS]	computer science
关键词[WOS]	learned dictionaries ; variable selection ; regression ; reconstruction ; algorithms ; domain
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000317156200002
公开日期	2015-06-30
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/23478]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	Chinese Acad Sci, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Lu, Xiaoqiang,Yuan, Yuan,Yan, Pingkun. Sparse coding for image denoising using spike and slab prior[J]. neurocomputing,2013,106:12-20.
APA	Lu, Xiaoqiang,Yuan, Yuan,&Yan, Pingkun.(2013).Sparse coding for image denoising using spike and slab prior.neurocomputing,106,12-20.
MLA	Lu, Xiaoqiang,et al."Sparse coding for image denoising using spike and slab prior".neurocomputing 106(2013):12-20.