Bayesian compressed sensing with new sparsity-inducing prior

Abstract

Sparse Bayesian learning (SBL) is a popular approach to sparse signal recovery in compressed sensing (CS). In SBL, the signal sparsity information is exploited by assuming a sparsity-inducing prior for the signal that is then estimated using Bayesian inference. In this paper, a new sparsity-inducing prior is introduced and efficient algorithms are developed for signal recovery. The main algorithm is shown to produce a sparser solution than existing SBL methods while preserving their desirable properties. Numerical simulations with one-dimensional synthetic signals and two-dimensional images verify our analysis and show that for sparse signals the proposed algorithm outperforms its SBL peers in both the signal recovery accuracy and computational speed. Its improved performance is also demonstrated in comparison with other state-of-the-art methods in CS.

11 pages, 6 figures, 1 table

References in corpus (4)

• Message Passing Algorithms for Compressed Sensing
• Spike and slab variable selection: Frequentist and Bayesian strategies
• Robustly Stable Signal Recovery in Compressed Sensing with Structured Matrix Perturbation
• Sparse Estimation using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models

Cited by in corpus (2)

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