Image super-resolution by dictionary concatenation and sparse representation with approximate L0 norm minimization
文献类型：期刊论文
| 作者 | Lu, Jinzheng1,2,3; Zhang, Qiheng1; Xu, Zhiyong1; Peng, Zhenming2 |
| 刊名 | Computers and Electrical Engineering
 |
| 出版日期 | 2012 |
| 卷号 | 38期号:5页码:1336-1345 |
| ISSN号 | 00457906 |
| 通讯作者 | Lu, J. (lujinzheng@163.com) |
| 中文摘要 | This paper proposes a different image super-resolution (SR) reconstruction scheme, based on the newly advanced results of sparse representation and the recently presented SR methods via this model. Firstly, we online learn a subsidiary dictionary with the degradation estimation of the given low-resolution image, and concatenate it with main one offline learned from many natural images with high quality. This strategy can strengthen the expressive ability of dictionary atoms. Secondly, the conventional matching pursuit algorithms commonly use a fixed sparsity threshold for sparse decomposition of all image patches, which is not optimal and even introduces annoying artifacts. Alternatively, we employ the approximate L0 norm minimization to decompose accurately the patch over its dictionary. Thus the coefficients of representation with variant number of nonzero items can exactly weight atoms for those complicated local structures of image. Experimental results show that the proposed method produces high-resolution images that are competitive or superior in quality to results generated by similar techniques. © 2011 Elsevier Ltd. All rights reserved. |
| 英文摘要 | This paper proposes a different image super-resolution (SR) reconstruction scheme, based on the newly advanced results of sparse representation and the recently presented SR methods via this model. Firstly, we online learn a subsidiary dictionary with the degradation estimation of the given low-resolution image, and concatenate it with main one offline learned from many natural images with high quality. This strategy can strengthen the expressive ability of dictionary atoms. Secondly, the conventional matching pursuit algorithms commonly use a fixed sparsity threshold for sparse decomposition of all image patches, which is not optimal and even introduces annoying artifacts. Alternatively, we employ the approximate L0 norm minimization to decompose accurately the patch over its dictionary. Thus the coefficients of representation with variant number of nonzero items can exactly weight atoms for those complicated local structures of image. Experimental results show that the proposed method produces high-resolution images that are competitive or superior in quality to results generated by similar techniques. © 2011 Elsevier Ltd. All rights reserved. |
| 学科主题 | Degradation |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000309693900026 |
| 源URL | [http://ir.ioe.ac.cn/handle/181551/5063]  |
| 专题 | 光电技术研究所_光电探测与信号处理研究室（五室） |
| 作者单位 | 1.5th Lab, Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, Sichuan Province, China 2.School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China 3.School of Information and Engineering, Southwest University of Science and Technology, Mianyang 621010, China |
| 推荐引用方式 GB/T 7714 | Lu, Jinzheng,Zhang, Qiheng,Xu, Zhiyong,et al. Image super-resolution by dictionary concatenation and sparse representation with approximate L0 norm minimization[J]. Computers and Electrical Engineering,2012,38(5):1336-1345. |
| APA | Lu, Jinzheng,Zhang, Qiheng,Xu, Zhiyong,&Peng, Zhenming.(2012).Image super-resolution by dictionary concatenation and sparse representation with approximate L0 norm minimization.Computers and Electrical Engineering,38(5),1336-1345. |
| MLA | Lu, Jinzheng,et al."Image super-resolution by dictionary concatenation and sparse representation with approximate L0 norm minimization".Computers and Electrical Engineering 38.5(2012):1336-1345. |
入库方式： OAI收割
来源：光电技术研究所
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