TY - JOUR
T1 - Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing
AU - Fang, Jun
AU - Zhang, Lizao
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6
Y1 - 2016/6
N2 - We consider the problem of recovering 2D block-sparse signals with unknown cluster patterns. The 2D block-sparse patterns arise naturally in many practical applications, such as foreground detection and inverse synthetic aperture radar imaging. To exploit the underlying block-sparse structure, we propose a 2D pattern-coupled hierarchical Gaussian prior model. The proposed pattern-coupled hierarchical Gaussian prior model imposes a soft coupling mechanism among neighboring coefficients through their shared hyperparameters. This coupling mechanism enables effective and automatic learning of the underlying irregular cluster patterns, without requiring any a priori knowledge of the block partition of sparse signals. We develop a computationally efficient Bayesian inference method, which integrates the generalized approximate message passing technique with the proposed prior model. Simulation results show that the proposed method offers competitive recovery performance for a range of 2D sparse signal recovery and image processing applications over the existing method, meanwhile achieving a significant reduction in the computational complexity.
AB - We consider the problem of recovering 2D block-sparse signals with unknown cluster patterns. The 2D block-sparse patterns arise naturally in many practical applications, such as foreground detection and inverse synthetic aperture radar imaging. To exploit the underlying block-sparse structure, we propose a 2D pattern-coupled hierarchical Gaussian prior model. The proposed pattern-coupled hierarchical Gaussian prior model imposes a soft coupling mechanism among neighboring coefficients through their shared hyperparameters. This coupling mechanism enables effective and automatic learning of the underlying irregular cluster patterns, without requiring any a priori knowledge of the block partition of sparse signals. We develop a computationally efficient Bayesian inference method, which integrates the generalized approximate message passing technique with the proposed prior model. Simulation results show that the proposed method offers competitive recovery performance for a range of 2D sparse signal recovery and image processing applications over the existing method, meanwhile achieving a significant reduction in the computational complexity.
KW - Pattern-coupled sparse Bayesian learning
KW - block-sparse structure
KW - expectation-maximization (EM)
KW - generalized approximate message passing (GAMP)
UR - http://www.scopus.com/inward/record.url?scp=84969988074&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969988074&partnerID=8YFLogxK
U2 - 10.1109/TIP.2016.2556582
DO - 10.1109/TIP.2016.2556582
M3 - Article
AN - SCOPUS:84969988074
SN - 1057-7149
VL - 25
SP - 2920
EP - 2930
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 6
M1 - 7457279
ER -