A Framework For Clustered And Skewed Sparse Signal Recovery

Keywords

approximate message passing; asymmetrical signal; Compressed sensing; expectation-maximization algorithms

Abstract

A novel framework, clustered-skew normal mixture-belief propagation, is developed to solve the reconstruction of undersampled clustered signals, where the magnitudes of signal coefficients in each cluster are distributed asymmetrically w.r.t the cluster mean. To address the skewness feature, a finite skew-normal density mixture is utilized to model the prior distribution, where the marginal posterior of the signal is inferred by an efficient approximate message-passing-based algorithm. An expectation-maximization-based algorithm is developed to estimate the mixture density. The clustered property is then modeled by the Potts model, and a loopy belief propagation algorithm is designed to promote the spatial feature. Experimental results show that our technique is highly effective and efficient in exploiting both the clustered feature and asymmetrical feature of the signals and outperforms many sophisticated techniques.

Publication Date

8-1-2018

Publication Title

IEEE Transactions on Signal Processing

Volume

66

Issue

15

Number of Pages

3972-3986

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TSP.2018.2839622

Socpus ID

85047630057 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85047630057

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