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题名:
Image super-resolution by dictionary concatenation and sparse representation with approximate L0 norm minimization
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
学科分类: Degradation
DOI: 10.1016/j.compeleceng.2011.11.026
通讯作者: Lu, J. (lujinzheng@163.com)
文章类型: 期刊论文
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.
收录类别: SCI ; Ei
项目资助者: West Light Personnel Training Project Grant of Chinese Academy of Sciences
语种: 英语
WOS记录号: WOS:000309693900026
ISSN号: 00457906
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.ioe.ac.cn/handle/181551/5063
Appears in Collections:光电探测与信号处理研究室(五室)_期刊论文

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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

Recommended Citation:
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.
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