[1]李订芳,江磊.基于l1与l0正则化的压缩感知数值算法[J].江西师范大学学报(自然科学版),2015,(03):281-285.
　LI Dingfang,JIANG Lei.The Numerical Algorithm of Compressive Sensing Based on l1 and l0 Regularization[J].Journal of Jiangxi Normal University:Natural Science Edition,2015,(03):281-285.
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基于l1与l0正则化的压缩感知数值算法()
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《江西师范大学学报》（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2015年03期
页码:
281-285
栏目:
出版日期:
2015-05-31

文章信息/Info

Title:
The Numerical Algorithm of Compressive Sensing Based on l1 and l0 Regularization
作者:
李订芳；江磊
武汉大学数学与统计学院,湖北 武汉 430072
Author(s):
LI Dingfang; JIANG Lei
关键词:
l1正则化;  l0正则化;  压缩感知;  稀疏恢复
Keywords:
l1 regularization;  l0 regularization;  compressive sensing;  sparse restoration (
分类号:
TP 391.41
文献标志码:
A
摘要:
针对压缩感知模型,讨论了基于l0正则化的正交匹配追踪算法(OMP)与基于l1正则化的同伦算法(HM)和迭代加权最小二乘法(IRLS).通过数值实验结果分析,验证了3种算法的有效性,且相对于2种基于l1正则化的算法,OMP算法的迭代次数与耗时更少,均方误差更小.
Abstract:
For compressive sensing,orthogonal matching pursuit algorithm(OMP)based on l0 norm regularization,homotopy algorithm(HM)based on l1 norm regularization and iteratively reweighted least squares algorithm(IRLS)based on l1 norm regularization are introduced.In numerical experiment,the validity of three algorithms above through analysis of numerical result are proved.Furthermore,for lower CPU cost and smaller mean square error,OMP is more efficient than other two algorithms based on l1 norm regularization.

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备注/Memo

备注/Memo:
国家自然科学基金(61271337)
更新日期/Last Update: 1900-01-01