@inproceedings{ab3ab0ccb8c547e5a60d2f65c19873ff,
title = "Autocalibrated signal reconstruction from linear measurements using adaptive GAMP",
abstract = "In this paper, we reconstruct signals from underdetermined linear measurements where the componentwise gains of the measurement system are unknown a priori. The reconstruction is performed through an adaptation of the messagepassing algorithm called adaptive GAMP that enables joint gain calibration and signal estimation. To evaluate our approach, we apply it to the problem of sparse recovery and compare it against an ℓ1-based approach. We numerically show that adaptive GAMP yields excellent results even for a moderate amount of data. It approaches the performance of oracle GAMP where the gains are perfectly known asymptotically.",
keywords = "approximate message passing, blind learning, inverse problems, MRI, Sparsity",
author = "Kamilov, \{Ulugbek S.\} and Aurelien Bourquard and Emrah Bostan and Michael Unser",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6638801",
language = "English",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "5925--5928",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}