Efficient Recovery of Structured Sparse Signals via Approximate Message Passing with Structured Spike and Slab Prior

Xiangming Meng, Sheng Wu*, Michael Riis Andersen, Jiang Zhu, Zuyao Ni

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review


Due to limited volume, weight and power consumption, micro-satellite has to reduce data transmission and storage capacity by image compression when performs earth observation missions. However, the quality of images may be unsatisfied. This paper considers the problem of recovering sparse signals by exploiting their unknown sparsity pattern. To model structured sparsity, the prior correlation of the support is encoded by imposing a transformed Gaussian process on the spike and slab probabilities. Then, an efficient approximate message-passing algorithm with structured spike and slab prior is derived for posterior inference, which, combined with a fast direct method, reduces the computational complexity significantly. Further, a unified scheme is developed to learn the hyperparameters using expectation maximization (EM) and Bethe free energy optimization. Simulation results on both synthetic and real data demonstrate the superiority of the proposed algoritlun.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
Issue number6
Publication statusPublished - Jun 2018
MoE publication typeA1 Journal article-refereed


  • compressed sensing
  • structured sparsity
  • spike and slab prior
  • approximate message passing
  • expectation propagation

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