KPLS-based image super-resolution using clustering and weighted boosting
文献类型：期刊论文
| 作者 | Li Xiaoyan; He Hongjie; Yin Zhongke; Chen Fan; Cheng Jun |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2015 |
| 英文摘要 | Kernel partial least squares (KPLS) algorithm for super-resolution (SR) has carried out a regression model to estimate a high-resolution (HR) feature patch from its corresponding low-resolution (LR) feature patch using a training database. However, KPLS may be time-consuming in the neighbor search and use of principal components. In this paper we propose a,clustering and weighted boosting (CWB) framework to improve the efficiency in KPLS regression model construction without reducing SR reconstruction quality. First, the training LR-HR feature patch pairs are divided into a certain number of clusters. For each test LR feature patch, the neighbor search in the selected cluster saves more computational costs than that in the whole training database. Second, a weighted boosting scheme is used to adaptively construct the KPLS regression model with the best number of principal components (BNPC). Experimental results on natural scene images suggest that the proposed CWB method can effectively improve the efficiency of KPLS-based SR method while preserving reconstruction quality, and achieve better performance than the conventional KPLS method. (C) 2014 Elsevier B.V. All rights reserved. |
| 收录类别 | SCI |
| 原文出处 | http://www.sciencedirect.com/science/article/pii/S0925231214009771 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/6683]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | NEUROCOMPUTING |
| 推荐引用方式 GB/T 7714 | Li Xiaoyan,He Hongjie,Yin Zhongke,et al. KPLS-based image super-resolution using clustering and weighted boosting[J]. NEUROCOMPUTING,2015. |
| APA | Li Xiaoyan,He Hongjie,Yin Zhongke,Chen Fan,&Cheng Jun.(2015).KPLS-based image super-resolution using clustering and weighted boosting.NEUROCOMPUTING. |
| MLA | Li Xiaoyan,et al."KPLS-based image super-resolution using clustering and weighted boosting".NEUROCOMPUTING (2015). |
入库方式： OAI收割
来源：深圳先进技术研究院
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