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Signal reconstruction in the presence of side information: The impact of projection kernel design

Chen, M-Y; Renna, Francesco; Rodrigues, Miguel; (2016) Signal reconstruction in the presence of side information: The impact of projection kernel design. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). (pp. pp. 4618-4622). IEEE Green open access

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Abstract

This paper investigates the impact of projection design on the reconstruction of high-dimensional signals from low-dimensional measurements in the presence of side information. In particular, we assume that both the signal of interest and the side information are described by a joint Gaussian mixture model (GMM) distribution. Sharp necessary and sufficient conditions on the number of measurements needed to guarantee that the average reconstruction error approaches zero in the low-noise regime are derived, for both cases when the side information is available at the decoder or at the decoder and encoder. Numerical results are also presented to showcase the impact of projection design on applications with real imaging data in the presence of side information.

Type: Proceedings paper
Title: Signal reconstruction in the presence of side information: The impact of projection kernel design
Event: ICASSP 2016: The 41st International Conference on Acoustics, Speech and Signal Processing
Location: Shanghai, China
Dates: 20 March 2016 - 25 March 2016
ISBN-13: 978-1-4799-9988-0
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ICASSP.2016.7472552
Publisher version: http://ieeexplore.ieee.org/document/7472552/
Language: English
Additional information: Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Decoding, Kernel, Image reconstruction, Atmospheric measurements, Particle measurements, Gaussian mixture model, mixture models, decoding, encoding, Gaussian distribution, Gaussian processes, image reconstruction, signal reconstruction, imaging data, encoder, decoder, low-noise regime, GMM distribution, Gaussian mixture model, low-dimensional measurement, high-dimensional signal, projection kernel design impact, side information, minimum mean-squared error, Kernel design, side information, compressive sensing, Gaussian mixture models
UCL classification: UCL
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/1530190
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