Two-Dimensional Pattern-Coupled Sparse Bayesian Learning via Generalized Approximate Message Passing

Jun Fang, Lizao Zhang, Hongbin Li

Research output: Contribution to journalArticlepeer-review

79 Scopus citations

Abstract

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.

Original languageEnglish
Article number7457279
Pages (from-to)2920-2930
Number of pages11
JournalIEEE Transactions on Image Processing
Volume25
Issue number6
DOIs
StatePublished - Jun 2016

Keywords

  • Pattern-coupled sparse Bayesian learning
  • block-sparse structure
  • expectation-maximization (EM)
  • generalized approximate message passing (GAMP)

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