Model based variational Bayesian compressive sensing using heavy tailed sparse prior

作者：Sadeghigol Zahra; Kahaei Mohammad Hossein^*; Haddadi Farzan

来源：Signal Processing: Image Communication , 2016, 41: 158-167.

DOI：10.1016/j.image.2015.09.008

• Bayesian compressive sensing
• Hidden Markov tree
• Statistical signal modelling
• Variational Bayes inference

In this paper, a novel multiscale model-based Bayesian compressive sensing is investigated using variational Bayesian inference in the complex wavelet domain. This model preserves the structural information by two-state signal noise Hidden Markov Tree (HMT). Tree structured hierarchical Generalized Double Pareto (GDP) distribution is used to model the sparsity of the signal. Using the Variational Bayes (VB) inference procedure a closed-form solution is obtained for model parameters. Experimental results in compressive sensing application show that the reconstruction error and CPU time of the proposed algorithm is lower compared to the other. well-known algorithms.

• 出版日期2016-2