Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
S. C. Birgmeier, N. Görtz:
"Exploiting General Multi-Dimensional Priors in Compressed-Sensing Reconstruction";
Vortrag: International ITG Conference on Systems, Communications and Coding (SCC 2019),
Rostock, Germany;
12.02.2019
- 14.02.2019; in: "Proceedings International ITG Conference on Systems, Communications and Coding (SCC 2019)",
VDE-Verlag,
(2019),
ISBN: 9783800748624;
S. 113
- 118.
Kurzfassung deutsch:
Message passing based algorithms have been shown to perform well in terms of minimum mean-squared error for high-dimensional signals composed of independent and identically distributed one-dimensional and sparse components. These conditions limit the applicability and performance of these algorithms since dependencies among components are not used during recovery. A detailed derivation is given that, as a novelty, extends the known derivation of the conventional Bayesian
Approximate Message Passing scheme (BAMP) to general multi-dimensional priors. The proposed algorithms significantly reduce the number of samples required for reconstruction compared to methods which do not exploit dependencies. Applications include multiple-measurement vector (MMV) problems, group sparsity as well as symbol recovery in MIMO systems and reconstruction in the case of general, non-sparse dependencies between components.
https://ieeexplore.ieee.org/document/8661317
Kurzfassung englisch:
Message passing based algorithms have been shown to perform well in terms of minimum mean-squared error for high-dimensional signals composed of independent and identically distributed one-dimensional and sparse components. These conditions limit the applicability and performance of these algorithms since dependencies among components are not used during recovery. A detailed derivation is given that, as a novelty, extends the known derivation of the conventional Bayesian
Approximate Message Passing scheme (BAMP) to general multi-dimensional priors. The proposed algorithms significantly reduce the number of samples required for reconstruction compared to methods which do not exploit dependencies. Applications include multiple-measurement vector (MMV) problems, group sparsity as well as symbol recovery in MIMO systems and reconstruction in the case of general, non-sparse dependencies between components.
https://ieeexplore.ieee.org/document/8661317
Schlagworte:
Signal Processing / Compressed Sensing / Approximate Message Passing
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.30420/454862020