Title :
Fast lp-sparse Bayesian learning for compressive sensing reconstruction
Author :
Wu, Jiao ; Liu, Fang ; Jiao, Lc
Author_Institution :
Coll. of Sci., China Jiliang Univ., Hangzhou, China
Abstract :
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-lp-SBL) 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.
Keywords :
belief networks; data compression; learning (artificial intelligence); maximum likelihood estimation; signal reconstruction; CS reconstruction algorithm; SF-lp-SBL algorithm; compressive sensing reconstruction; fast sequential learning scheme; maximum a posteriori estimation; sparse Bayesian learning; sparse linear model; stage-wise strategy; Bayesian methods; Compressed sensing; Educational institutions; Image reconstruction; Optimization; Signal processing algorithms; Vectors; compressive sensing; maximum a posteriori estimation; sparse Bayesian learning;
Conference_Titel :
Image and Signal Processing (CISP), 2011 4th International Congress on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9304-3
DOI :
10.1109/CISP.2011.6100618