Model based variational Bayesian compressive sensing using heavy tailed sparse prior
作者：
Highlights：
• We have proposed a new model based Bayesian Compressive Sensing (CS) based on generalized double Pareto.
• We have derived all posterior pdfs׳ parameters of this new model in the closed form.
• We examine the proposed algorithm in two scenarios: without noise and noisy observation.
• In both scenarios, the proposed algorithm outperforms the well-known CS method.
摘要
Highlights•We have proposed a new model based Bayesian Compressive Sensing (CS) based on generalized double Pareto.•We have derived all posterior pdfs׳ parameters of this new model in the closed form.•We examine the proposed algorithm in two scenarios: without noise and noisy observation.•In both scenarios, the proposed algorithm outperforms the well-known CS method.
论文关键词：Bayesian compressive sensing,Hidden Markov tree,Statistical signal modelling,Variational Bayes inference
论文评审过程：Received 17 February 2015, Revised 13 September 2015, Accepted 14 September 2015, Available online 3 November 2015, Version of Record 8 February 2016.
论文官网地址：https://doi.org/10.1016/j.image.2015.09.008
