Fast lp-Sparse Bayesian Learning for Compressive Sensing Reconstruction

摘要：

The problem of compressive sensing reconstruction can be come down to the problem of solving a sparse linear model. In this paper, the lp(0<p≤1)-type prior that encourages sparsity of the weights is used to the sparse linear model. Motivated by maximum a posteriori estimation and sparse Bayesian learning, a stage-wise fast lp-sparse Bayesian learning (SF-lPSBL) algorithm is proposed through integrating with a fast sequential learning scheme and a stage-wise strategy. The experiments demonstrate that SF-lp-SBL is a fast and effective CS reconstruction algorithm.

关键词： compressive sensing maximum a posteriori estimation sparse Bayesian learning

作者: Jiao Wu Fang Liu Lc Jiao

作者单位: College of Sciences, China Jiliang University, Hangzhou, P.R. China School of Computer Science and T School of Computer Science and Technology, Xidian University, Xian, P.R. China Key Laboratory of In Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China,

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1929-1933

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）