压缩感知中确定性测量矩阵构造算法综述
A Survey on Deterministic Measurement Matrix Construction Algorithms in Compressive Sensing
查看参考文献50篇
|
文摘
|
测量矩阵在压缩感知中起着关键性的作用,其性能会影响原始信号的压缩与重构。现有的测量矩阵多数为随机的,它们在实际应用中有存储量大、效率低等缺点,且在硬件上难以实现,故构造确定性测量矩阵对压缩感知理论的推广与应用具有重要的意义。本文回顾了国内外学者在确定性测量矩阵构造方面的研究,着重对目前已有的构造算法进行详细的介绍和分类,最后根据多种指标综合评述了各种算法的性能。 |
|
其他语种文摘
|
Measurement matrix ,whose performance can affect the compression and reconstruction of original signal ,plays a key role in compressive sensing .Most of the existing measurement matrices are random ones ,which have shortcomings in practical application ,such as large storage capacity ,low efficiency and difficulty when implemented in the hardware .Therefore ,it is of im-portant practical significance to construct deterministic measurement matrix for the promotion and application of the compressive sensing theory .In this paper ,the existing construction algorithms for deterministic measurement matrix are reviewed ,introduced and classified in detail .Finally the performances of all algorithms are summarized in terms of common indicators . |
|
来源
|
电子学报
,2013,41(10):2041-2050 【核心库】
|
|
DOI
|
10.3969/j.issn.0372-2112.2013.10.027
|
|
关键词
|
压缩感知
;
确定性测量矩阵
;
有限等距性质
;
信号重构
|
|
地址
|
哈尔滨工业大学控制科学与工程系, 黑龙江, 哈尔滨, 150001
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
0372-2112 |
|
学科
|
电子技术、通信技术 |
|
基金
|
国家自然科学基金
|
|
文献收藏号
|
CSCD:4967653
|
参考文献 共
50
共3页
|
|
1.
Donoho D L. Compressed sensing.
IEEE Transactions on Information Theory,2006,52(4):1289-1306
|
CSCD被引
2930
次
|
|
|
|
|
2.
Candes E. Compressive sampling.
Proceeding of IEEE ICM'06,2006:1433-1452
|
CSCD被引
1
次
|
|
|
|
|
3.
马坚伟. 压缩感知及其应用:从稀疏约束到低秩约束优化.
信号处理,2012,28(5):609-623
|
CSCD被引
36
次
|
|
|
|
|
4.
Aharon M. The K-SVD:An algorithm for designing of over complete dictionaries for sparse representations.
IEEE Transactions on Image Processing,2006,54(11):4311-4322
|
CSCD被引
871
次
|
|
|
|
|
5.
Sigg C D. Learning dictionaries with bounded self-coherence.
IEEE Signal Processing Letter,2012,19(12):861-864
|
CSCD被引
5
次
|
|
|
|
|
6.
Mallat S.
A Wavelet Tour of Signal Processing,1996
|
CSCD被引
5
次
|
|
|
|
|
7.
Rauhut H. Compressed sensing and redundant dictionaries.
IEEE Transactions on Information Theory,2008,54(5):2210-2219
|
CSCD被引
75
次
|
|
|
|
|
8.
Duarte Marco F.
Introduction to Compressive Sensing,2012
|
CSCD被引
1
次
|
|
|
|
|
9.
焦李成. 压缩感知回顾与展望.
电子学报,2011,7(7):1651-1662
|
CSCD被引
180
次
|
|
|
|
|
10.
杨海蓉. 压缩传感理论与重构算法.
电子学报,2011,39(1):142-148
|
CSCD被引
74
次
|
|
|
|
|
11.
Kim S J. An interior-point method for large-scale l_1 regularized least squares.
IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617
|
CSCD被引
151
次
|
|
|
|
|
12.
Fiqueiredo M A T. Gradient projection for sparse reconstruction:Application to compressed sensing and other inverse problems.
IEEE Journal of Selected Topics in Signal Processing,2007,1(4):586-597
|
CSCD被引
384
次
|
|
|
|
|
13.
Tropp J A. Signal recovery from random measurements via orthogonal matching pursuit.
IEEE Transactions on Information Theory,2007,53(12):4655-4666
|
CSCD被引
1101
次
|
|
|
|
|
14.
Dai W. Subspace pursuit for compressive sensing signal reconstruction.
IEEE Transactions on Information Theory,2009,55(5):2230-2249
|
CSCD被引
252
次
|
|
|
|
|
15.
Needell D. CoSaMP:Iterative signal recovery from incomplete and inaccurate samples.
Applied and Computational Harmonic Analysis,2008,26(3):301-321
|
CSCD被引
355
次
|
|
|
|
|
16.
Chen B S. Atomic decomposition by basis pursuit.
SIAM Journal on Scientific Computing,1998,20(1):33-61
|
CSCD被引
2
次
|
|
|
|
|
17.
李志林. 一种有效的压缩感知图像重建算法.
电子学报,2011,39(12):2796-2800
|
CSCD被引
10
次
|
|
|
|
|
18.
Candes E. Robust uncertainty principles:Exact signal reconstruction from highly incomplete frequency information.
IEEE Transactions on Information Theory,2006,52(2):489-509
|
CSCD被引
1361
次
|
|
|
|
|
19.
Tropp J A. Greed is good:Algorithmic results for sparse approximation.
IEEE Transactions on Information Theory,2004,50(10):2231-2242
|
CSCD被引
165
次
|
|
|
|
|
20.
Sustik M A. On the existence of equiangular tight frames.
Linear Algebra and its Applications,2007,426(2/3):619-635
|
CSCD被引
5
次
|
|
|
|
|
了解气象卫星更多信息，请访问