NDLI: Compressive sensing for cluster structured sparse signals: variational Bayes approach

Content Provider	IET Digital Library
Author	Yu, Lei Wei, Chen Jia, Jinyuan Sun, Hong
Abstract	Compressive sensing (CS) provides a new paradigm of sub-Nyquist sampling which can be considered as an alternative to Nyquist sampling theorem. In particular, providing that signals are with sparse representations in some domain, information can be perfectly preserved even with small amount of measurements captured by random projections. Besides sparsity prior of signals, the inherent structure property behind some specific signals is often exploited to enhance the reconstruction accuracy. In this study, the authors are aiming to take into account the cluster structure property of sparse signals, of which the non-zero coefficients appear in clustered blocks. By modelling simultaneously both sparsity and cluster prior within a hierarchical statistical Bayesian framework, a non-parametric algorithm can be obtained through variational Bayes approach to recover original sparse signals. The proposed algorithm could be slightly considered as a generalisation of Bayesian CS (BCS), but with a consideration on cluster property. Consequently, the performance of the proposed algorithm is at least as good as BCS, which is verified by the experimental results.
Starting Page	770
Ending Page	779
Page Count	10
ISSN	17519675
Volume Number	10
e-ISSN	17519683
Issue Number	Issue 7, Sep (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/10/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2014.0157
Journal	IET Signal Processing
Publisher Date	2016-09-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bayes Method Cluster Structured Sparse Signal Clustered Blocks Compressed Sensing Compressive Sensing Hierarchical Statistical Bayesian Framework Nonzero Coefficient Nyquist Sampling Theorem Random Projections Signal Processing And Detection Signal Processing Theory Sparse Representations Sparse Signal Statistics SubNyquist Sampling Variational Bayes Approach
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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