Institutional Repository of Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
| A novel super-resolution image and video reconstruction approach based on Newton-Thiele's rational kernel in sparse principal component analysis | |
He, Lei1; Tan, Jieqing1; Huo, Xing1; Xie, Chengjun2
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| 2017-04-01 | |
| 发表期刊 | MULTIMEDIA TOOLS AND APPLICATIONS
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| 摘要 | In this paper, we propose a new image and video sequences reconstruction approach, where the Newton-Thiele's vector valued rational interpolation is combined with the sparse principal component analysis. Through observation of the degraded model, the reconstruction scheme is performed by two steps. Firstly, the sparse principal component analysis and the linear minimum mean square-error estimation method are used to remove the noise from the degraded image. And then, the Newton-Thiele's vector valued rational interpolation is used to magnify the denoising result, by which the details and texture regions of image can be well preserved. By using this novel reconstruction model by Newton-Thiele's rational kernel in sparse principal component analysis, the final reconstructed results not only have good visual effect, but also have rich texture details. In order to show the effectiveness and robustness of the proposed method, we have done plenty of experiments on images and video sequences, and the experimental results show that the proposed method can produce better high-quality resolution results, as compared with the state-of-the-art methods. |
| 文章类型 | Article |
| 关键词 | Newton-thiele Super-resolution Linear Minimum Mean Square-error Estimation Sparse Principal Component Analysis |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1007/s11042-016-3557-1 |
| 关键词[WOS] | INTERPOLATION ; HALLUCINATION ; REMOVAL |
| 收录类别 | SCI |
| 语种 | 英语 |
| 项目资助者 | National Natural Science Foundation of China(61472466 ; National Natural Science Foundation of China(61472466 ; National Natural Science Foundation of China(61472466 ; National Natural Science Foundation of China(61472466 ; NSFC-Guangdong Joint Foundation(U1135003) ; NSFC-Guangdong Joint Foundation(U1135003) ; NSFC-Guangdong Joint Foundation(U1135003) ; NSFC-Guangdong Joint Foundation(U1135003) ; Anhui Provincial Natural Science Foundation(1508085QF128) ; Anhui Provincial Natural Science Foundation(1508085QF128) ; Anhui Provincial Natural Science Foundation(1508085QF128) ; Anhui Provincial Natural Science Foundation(1508085QF128) ; Fundamental Research Funds for the Central Universities(JZ2015HGXJ0175) ; Fundamental Research Funds for the Central Universities(JZ2015HGXJ0175) ; Fundamental Research Funds for the Central Universities(JZ2015HGXJ0175) ; Fundamental Research Funds for the Central Universities(JZ2015HGXJ0175) ; 61502141 ; 61502141 ; 61502141 ; 61502141 ; 61070227) ; 61070227) ; 61070227) ; 61070227) |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000399016300016 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.hfcas.ac.cn:8080/handle/334002/31833 |
| 专题 | 中科院合肥智能机械研究所 |
| 作者单位 | 1.Hefei Univ Technol, Sch Comp & Informat, Sch Math, Hefei 230009, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Lei,Tan, Jieqing,Huo, Xing,et al. A novel super-resolution image and video reconstruction approach based on Newton-Thiele's rational kernel in sparse principal component analysis[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2017,76(7):9463-9483. |
| APA | He, Lei,Tan, Jieqing,Huo, Xing,&Xie, Chengjun.(2017).A novel super-resolution image and video reconstruction approach based on Newton-Thiele's rational kernel in sparse principal component analysis.MULTIMEDIA TOOLS AND APPLICATIONS,76(7),9463-9483. |
| MLA | He, Lei,et al."A novel super-resolution image and video reconstruction approach based on Newton-Thiele's rational kernel in sparse principal component analysis".MULTIMEDIA TOOLS AND APPLICATIONS 76.7(2017):9463-9483. |
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