Bayesian Compressed Sensing with Heterogeneous Side Information

Evangelos Zimos, João F. C. Mota, Miguel Raul Dias Rodrigues, Nikos Deligiannis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

The classical compressed sensing (CS) paradigm can be modified so as to leverage a signal correlated to the signal of interest, called side information, which is assumed to be provided a priori at the decoder in order to aid reconstruction. In this work, we propose a novel CS reconstruction method based on belief propagation principles, which manages to exploit side information generated from a diverse (or heterogeneous) data source by using the statistical model of copula functions. Through simulations, we demonstrate that the proposed method yields significant reduction in the mean-squared error of the reconstructed signal as compared to state-of-the-art methods in classical compressed sensing and compressed sensing with side information.

Original languageEnglish
Title of host publication2016 Data Compression Conference
PublisherIEEE
Pages191-200
Number of pages10
ISBN (Electronic)9781509018536
DOIs
Publication statusPublished - Dec 2016
Event2016 Data Compression Conference - Snowbird, United States
Duration: 30 Mar 20161 Apr 2016

Conference

Conference2016 Data Compression Conference
Abbreviated titleDCC 2016
CountryUnited States
CitySnowbird
Period30/03/161/04/16

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Zimos, E., Mota, J. F. C., Rodrigues, M. R. D., & Deligiannis, N. (2016). Bayesian Compressed Sensing with Heterogeneous Side Information. In 2016 Data Compression Conference (pp. 191-200). [7786163] IEEE. https://doi.org/10.1109/DCC.2016.44