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Talks and Poster Presentations (with Proceedings-Entry):

O. Musa, G. Hannak, N. Görtz:
"Efficient Recovery from Noisy Quantized Compressed Sensing Using Generalized Approximate Message Passing";
Poster: 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Curacao, Durch Antilles; 12-10-2017 - 12-13-2017; in: "Proeceedings International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)", IEEE, (2017), ISBN: 978-1-5386-1251-4; 1 - 5.



English abstract:
Compressed sensing (CS) is a novel technique that
allows for stable reconstruction with sampling rate lower than
Nyquist rate if the unknown vector is sparse. In many practical
applications CS measurements are first scalar quantized and later
corrupted in different ways. Reconstruction by conventional tech-
niques on such highly distorted measurements will result in poor
accuracy. To address this problem, we use the well established
generalized approximate message passing (GAMP) algorithm
and tailor it for quantized CS measurements corrupted with
noise. We provide the necessary expressions for the nonlinear
updates for different noise models, namely the symmetric discrete
memoryless channel (SDMC) and the additive white Gaussian
noise (AWGN) channel. Numerical results show superiority of
the GAMP algorithm compared to conventional reconstruction
algorithms in both SDMC and AWGN channels.

German abstract:
Compressed sensing (CS) is a novel technique that
allows for stable reconstruction with sampling rate lower than
Nyquist rate if the unknown vector is sparse. In many practical
applications CS measurements are first scalar quantized and later
corrupted in different ways. Reconstruction by conventional tech-
niques on such highly distorted measurements will result in poor
accuracy. To address this problem, we use the well established
generalized approximate message passing (GAMP) algorithm
and tailor it for quantized CS measurements corrupted with
noise. We provide the necessary expressions for the nonlinear
updates for different noise models, namely the symmetric discrete
memoryless channel (SDMC) and the additive white Gaussian
noise (AWGN) channel. Numerical results show superiority of
the GAMP algorithm compared to conventional reconstruction
algorithms in both SDMC and AWGN channels.

Keywords:
Compressed sensing, quantization, generalized approximate message passing, Bernoulli-Gauss mixture


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/CAMSAP.2017.8313153


Created from the Publication Database of the Vienna University of Technology.