De Castro Mota, JF;
Zimos, E;
Deligiannis, N;
Rodrigues, M;
(2016)
Bayesian compressed sensing with heterogeneous side information.
In:
Proceedings of the 2016 Data Compression Conference (DCC).
(pp. pp. 191-200).
IEEE
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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.
Type: | Proceedings paper |
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Title: | Bayesian compressed sensing with heterogeneous side information |
Event: | 2016 Data Compression Conference (DCC) |
Location: | Snowbird (UT), USA |
Dates: | 30th March - 1st April 2016 |
ISBN-13: | 978-1-5090-1853-6 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/DCC.2016.44 |
Publisher version: | https://doi.org/10.1109/DCC.2016.44 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Silicon, Sensors, Sparse matrices, Image reconstruction, Bayes methods, Compressed sensing, Probability density function |
UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/1529229 |
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