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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut1-17
Sivumäärä17
JulkaisuCHINA COMMUNICATIONS
Vuosikerta15
Numero6
DOI - pysyväislinkit
TilaJulkaistu - kesäkuuta 2018
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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