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Bayesian compressive sensing for cluster structured sparse signals

机译：集群结构稀疏信号的贝叶斯压缩感知

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摘要

In traditional framework of compressive sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Other than sparse prior, structures on the sparse pattern of the signal have also been used as an additional prior, called model-based compressive sensing, such as clustered structure and tree structure on wavelet coefficients. In this paper, the cluster structured sparse signals are investigated. Under the framework of Bayesian compressive sensing, a hierarchical Bayesian model is employed to model both the sparse prior and cluster prior, then Markov Chain Monte Carlo (MCMC) sampling is implemented for the inference. Unlike the state-of-the-art algorithms which are also taking into account the cluster prior, the proposed algorithm solves the inverse problem automatically—prior information on the number of clusters and the size of each cluster is unknown. The experimental results show that the proposed algorithm outperforms many state-of-the-art algorithms.
机译：在传统的压缩感知（CS）框架中，仅采用时域或频域上的信号稀疏先验来保证精确的逆恢复。除了稀疏先验，信号稀疏模式上的结构也已被用作附加的先验，称为基于模型的压缩感知，例如小波系数上的聚类结构和树形结构。本文研究了簇结构稀疏信号。在贝叶斯压缩感知的框架下，采用分层贝叶斯模型对稀疏先验和聚类先验进行建模，然后采用马尔可夫链蒙特卡罗（MCMC）采样进行推理。与也考虑了聚类先验的最新算法不同，所提出的算法会自动解决逆问题-有关聚类数量和每个聚类大小的先验信息是未知的。实验结果表明，该算法优于许多最新算法。

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  • 来源
    《Signal processing》 |2012年第1期|p.259-269|共11页
  • 作者

    L. Yu; H. Sun; J.P. Barbot; G. Zheng;

  • 作者单位

    E.I.S, Wuhan University, 129 Road of Luoyu, 430079 Wuhan, China,ECS-Lab ENSEA, 6 Avenue du Ponceau, 95014 Cergy-Pontoise, France;

    E.I.S, Wuhan University, 129 Road of Luoyu, 430079 Wuhan, China;

    ECS-Lab ENSEA, 6 Avenue du Ponceau, 95014 Cergy-Pontoise, France,Group Non-A, INRIA, 59000 Lille, France;

    Group Non-A, INRIA, 59000 Lille, France;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    compressive sensing; cluster structured sparse signals; hierarchical bayesian model; MCMC;

    机译：压缩感测集群结构的稀疏信号;分层贝叶斯模型;MCMC;

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