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基于傅里叶基的自适应压缩感知重构算法

吕方旭 张金成 王泉 王钰

吕方旭, 张金成, 王泉, 等 . 基于傅里叶基的自适应压缩感知重构算法[J]. 北京航空航天大学学报, 2014, 40(4): 544-550. doi: 10.13700/j.bh.1001-5965.2013.0332
引用本文: 吕方旭, 张金成, 王泉, 等 . 基于傅里叶基的自适应压缩感知重构算法[J]. 北京航空航天大学学报, 2014, 40(4): 544-550. doi: 10.13700/j.bh.1001-5965.2013.0332
Lü Fangxu, Zhang Jincheng, Wang Quan, et al. Adaptive recovery algorithm for compressive sensing based on Fourier basis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(4): 544-550. doi: 10.13700/j.bh.1001-5965.2013.0332(in Chinese)
Citation: Lü Fangxu, Zhang Jincheng, Wang Quan, et al. Adaptive recovery algorithm for compressive sensing based on Fourier basis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(4): 544-550. doi: 10.13700/j.bh.1001-5965.2013.0332(in Chinese)

基于傅里叶基的自适应压缩感知重构算法

doi: 10.13700/j.bh.1001-5965.2013.0332
详细信息
    作者简介:

    吕方旭(1988- ),男,陕西渭南人,硕士生,lvfangxu1988@163.com.

  • 中图分类号: TP202

Adaptive recovery algorithm for compressive sensing based on Fourier basis

  • 摘要: 在压缩感知中,为了提高含噪信号的重构精度,提出了基于傅里叶基的稀疏度自适应匹配追踪算法.该算法在重构过程中采用相关系数作为匹配准则的基础上,创新性地利用傅里叶变换的共轭对称性,进一步严格控制索引值加入支撑集的过程;同时利用余量能量和余量能量变化率双门限作为停止迭代的依据;最后将估计的傅里叶域中的信号逆变换得到时域的重构信号.仿真实验表明,在同等噪声污染的情况下,该算法与同类算法相比有较高的重构精度.

     

  • [1] Donoho D.Compressed sensing[J].IEEE Transactions on Information Theory, 2006, 52(4):1289-1306
    [2] Candès E, Wakin M.An introduction to compressive sampling[J].IEEE Signal Processing Magazine, 2008, 25(2):21-30
    [3] 林杰, 石光明.基于信息自由度采样的信号重构方法研究进展[J].电子学报, 2012, 40(8):1640-1649 Lin Jie, Shi Guangming.Research advances in reconstruction methods based on information degree-of-freedom sampling[J].Acta Electronica Sinica, 2012, 40(8):1640-1649(in Chinese)
    [4] 焦李成, 杨淑媛, 刘芳, 等.压缩感知回顾与展望[J].电子学报, 2011, 39(7):1651-1662 Jiao Licheng, Yang Shuyuan, Liu Fang, et al.Development and prospect of compressive sensing[J].Acta Electronica Sinica, 2011, 39(7):1651-1662(in Chinese)
    [5] Chen S B, Donoho D L, Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Journal on Scientific Computing, 1998, 20(1):33-61
    [6] Koh K, Kim S J, Boyd S.An interior-point method for large-scale l1 regularized least squares[J].IEEE Journal of Machine Learing Reserach, 2007, 8(7):1519-1555
    [7] Fiqueiredo M A T, Nowak R D, Wright S J.Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J].IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4):586-597
    [8] Gilbert A C, Muthukrishnan S, Strauss M.Improved time bounds for near-optimal sparse Fourier representation[C]//Proceedings of SPIE:Wavelets XI.Bellingham WA:International Society for Optical Engineering, 2005:14-15
    [9] Gilbert A C, Strauss M J, Tropp J A, et al.Algorithmic linear dimension reduction in the l1 norm for sparse vectors[EB/OL].2006-08-19[2013-08-20].http://arxiv.org/abs/cs/0608079
    [10] Neff R, Zakhor A.Very low bit rate video coding based on matching pursuits[J].IEEE Transactions on Circuits and Systems for Video Technology, 1997, 7(1):158-171
    [11] Candes E, Romberg J.Sparsity and incoherence in compressive sampling[J].Inverse Problems, 2007, 23(3):969-985
    [12] Donoho D L, Tsaig Y, Drori I, et al.Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit[J].IEEE, 2012, 58(2):1094-1121
    [13] Needell D, Vershynin R.Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J].IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2):310-316
    [14] Dai W, Milenkovic O.Subspace pursuit for compressive sensing signal reconstruction[J].IEEE Trans on Information Theory, 2009, 55(5):2230-2249
    [15] 甘伟, 许录平, 苏哲.一种压缩感知重构算法[J].电子与信息学报, 2010, 32(9):2151-2155 Gan Wei, Xu Luping Su Zhe.A recovery-algorithm for compressed sensing[J].Journal of Electronics & Information Technology, 2010, 32(9):2151-2155(in Chinese)
    [16] Do T T, Gan L, Nguyen N, et al.Sparsity adaptive matching pursuit algorithm for practical compressed sensing[C]//Asilomar Conference on Signals, Systems and Computer.Piscataway, NJ:IEEE, 2008:581-587
    [17] 甘伟, 许录平, 张华, 等.一种贪婪自适应压缩感知重构[J].西安电子科技大学学报:自然科学版, 2012, 39(3): 50-57, 79 Gan Wei, Xu Luping, Zhang Hua, et al.Greedy adaptive recovery algorithm for compressed sensing[J].Journal of Xidian University:Natural Science, 2012, 39(3):50-57, 79(in Chinese)
    [18] 朱延万, 赵拥军, 孙兵.一种改进的稀疏度自适应匹配追踪算法[J].信号处理, 2012, 28(1):80-86 Zhu Yanwan, Zhao Yongjun, Sun Bing.A modified sparsity adaptive matching pursuit algorithm[J].Signal Processing, 2012, 28(1):80-86(in Chinese)
    [19] Donoho D L, Tsaig Y.Extensions of compressed sensing[J].Signal Processing, 2006, 86(3):533-548
    [20] 高西全, 丁玉美.数字信号处理[M].西安:西安电子科技大学出版社, 2008:84-87 Gao Xiquan, Ding Yumei.Digital signal processing[M].Xi'an:Xidian University Press, 2008:84-87(in Chinese)
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出版历程
  • 收稿日期:  2013-06-09
  • 网络出版日期:  2014-04-20

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