Sparse coding for image denoising using spike and slab prior
文献类型：期刊论文
| 作者 | Lu, Xiaoqiang ; Yuan, Yuan; Yan, Pingkun
|
| 刊名 | neurocomputing
 |
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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