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Bayesian Compressed Sensing with Heterogeneous Side Information Host Publication: IEEE Data Compression Conference DCC 2016 Authors: E. Zimos, J. Mota, M. Rodrigues and N. Deligiannis Publication Year: 2016 Number of Pages: 10
Abstract: The classical compressed sensing (CS) paradigm can be modi ed 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.
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