Approximate Message Passing-based Compressed Sensing Reconstruction with Generalized Elastic Net Prior

Resource type
Date created
2015
Authors/Contributors
Author: Liang, Jie
Author: Wang, Xing
Abstract
In this paper, we study the compressed sensing reconstruction problem with generalized elastic net prior (GENP), where a sparse signal is sampled via a noisy underdetermined linear observation system, and an additional initial estimation of the signal (the GENP) is available during the reconstruction. We first incorporate the GENP into the LASSO and the approximate message passing (AMP) frameworks, denoted by GENP-LASSO and GENP-AMP respectively. We then focus on GENP-AMP and investigate its parameter selection, state evolution, and noise-sensitivity analysis. A practical parameterless version of the GENP-AMP is also developed, which does not need to know the sparsity of the unknown signal and the variance of the GENP. Simulation results with 1-D data and two different imaging applications are presented to demonstrate the efficiency of the proposed schemes.
Document
Published as
Lian, Jie; Wang, Xing. Approximate Message Passing-based Compressed Sensing Reconstruction with Generalized Elastic Net Prior. Signal Processing: Image Communication 37 (2015) 19–33. doi:10.1016/j.image.2015.06.011
Publication title
Signal Processing: Image Communication
Document title
Approximate Message Passing-based Compressed Sensing Reconstruction with Generalized Elastic Net Prior
Date
2015
Volume
37
First page
19
Last page
33
Publisher DOI
10.1016/j.image.2015.06.011
Copyright statement
Copyright is held by the author(s). Copyright of final published version is held by Elsevier.
Scholarly level
Peer reviewed?
Yes
Language
Member of collection
Attachment Size
si-amp.pdf 983.6 KB