Reconstruction of sparse signals from highly corrupted measurements by nonconvex minimization (CROSBI ID 610571)
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Podaci o odgovornosti
Filipović, Marko
engleski
Reconstruction of sparse signals from highly corrupted measurements by nonconvex minimization
We propose a method for signal recovery in compressed sensing when measurements can be highly corrupted. It is based on lp minimization for 0 < p <= 1. Since it was shown that lp minimization performs better than l1 minimization when there are no large errors, the proposed approach is a natural extension to compressed sensing with corruptions. We provide a theoretical justification of this idea, based on analogous reasoning as in the case when measurements are not corrupted by large errors. Better performance of the proposed approach compared to l1 minimization is illustrated in numerical experiments.
Compressive Sensing ; Sparse Signal Reconstruction ; Nonconvex Optimization ; Restricted Isometry
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Podaci o prilogu
3395-3399.
2014.
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objavljeno
Podaci o matičnoj publikaciji
Proceedings of the 2014 IEEE International Conference on ACoustics, Speech and Signal Processing (ICASSP)
Institute of Electrical and Electronics Engineers (IEEE)
Podaci o skupu
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
poster
04.05.2014-09.05.2014
Firenca, Italija