[1]封 磊,孙怀江.结合局部稀疏性和非局部相似性的盲压缩感知方法[J].南京理工大学学报(自然科学版),2017,41(04):399.[doi:10.14177/j.cnki.32-1397n.2017.41.04.001]
 Feng Lei,Sun Huaijiang.Blind compressive sensing method via local sparsity and nonlocal similarity[J].Journal of Nanjing University of Science and Technology,2017,41(04):399.[doi:10.14177/j.cnki.32-1397n.2017.41.04.001]
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结合局部稀疏性和非局部相似性的盲压缩感知方法()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年04期
页码:
399
栏目:
出版日期:
2017-08-31

文章信息/Info

Title:
Blind compressive sensing method via local sparsity and nonlocal similarity
文章编号:
1005-9830(2017)04-0399-06
作者:
封 磊孙怀江
南京理工大学 计算机科学与工程学院,江苏 南京 210094
Author(s):
Feng LeiSun Huaijiang
School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
盲压缩感知 稀疏表示 字典学习 非局部相似性 交替方向乘子法
Keywords:
blind compressive sensing sparse representation dictionary study nonlocal similarity alternating direction method of multipliers.
分类号:
TN911.72
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.001
摘要:
为了降低传统的盲压缩感知图像重建方法所需求的采样率,提出了一种新的盲压缩感知图像重建方法,该方法同时考虑局部图像块的稀疏性和非局部图像块间的相似性,另外选择交替方向乘子算法求解产生的非凸优化问题,实现了图像的准确重建。实验结果表明,在不损失图像重构质量的情况下,该方法能够显著地降低采样率。
Abstract:
In order to reduce the sampling rate of the traditional blind compressive sensing image recovery method,this paper proposes a novel blind compressive sensing image recovery approach.The method simultaneously exploits the local patch sparsity and nonlocal patch similarity.In addition,it employs an alternating direction method of multipliers to solve the resulting non-convex optimization problem.The method can accurately recover the original image.Experimental results have demonstrated that the proposed method can significantly reduce the sampling rate without sacrificing the quality of the reconstructed image.

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备注/Memo

备注/Memo:
收稿日期:2016-10-26 修回日期:2017-03-03
作者简介:封磊(1987-),男,博士生,主要研究方向:模式识别、压缩感知,E-mail:fenglei492327278@126.com; 通讯作者:孙怀江(1968-),男,教授,博士生导师,主要研究方向:Web智能、模式识别等,E-mail:sunhuaijiang@njust.edu.cn。
引文格式:封磊,孙怀江.结合局部稀疏性和非局部相似性的盲压缩感知方法[J].南京理工大学学报,2017,41(4):399-404.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-08-31