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Cauchy diversity measures: a novel methodology for enhancing sparsity in compressed sensing

机译:柯西多样性度量:一种增强压缩感知稀疏性的新颖方法

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摘要

As a new enchanting theory, compressed sensing (CS) demonstrates that a sparse signal can be recovered through a surprisingly small number of linear measurements by solving a problem of l1 norm minimisation (which can be thought as a special case of the signomial diversity measures). However, the traditional CS model with l1 norm minimisation can not fully exploit the sparsity especially when the degree of sparsity increases or the measurements number reduces. In this study, the Cauchy diversity measures is incorporated into the proposed model to deal with the above difficulties. The simulation results demonstrate that under the same condition, this new model offers a superior reconstruction precision compared with the common used signomial diversity measures.
机译:作为一种新的附魔理论,压缩感测(CS)表明,通过解决l 1 范数最小化问题(可以认为是一个信号多样性指标的特殊情况)。但是,传统的具有1 1 范数最小化的CS模型无法充分利用稀疏性,尤其是当稀疏度增加或测量次数减少时。在这项研究中,柯西多样性测度被纳入提出的模型中以解决上述困难。仿真结果表明,在相同条件下,该新模型与常用的信号多样性算法相比,具有更高的重构精度。

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  • 来源
    《Signal Processing, IET》 |2013年第9期|791-799|共9页
  • 作者

    Zhao G.; Shen F.; Wang Z.; Shi G.;

  • 作者单位

    School of Electronic Engineering, Xidian University, Xi'an, Shannxi, People's Republic of China|c|;

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