Title :
A reconstruction approach for noisy compressive sensing via iterative support detection
Author :
Wentao Zhang ; Yanjun Hu ; Fang Jiang ; Yao Wang
Author_Institution :
Key Lab. of Intell. Comput. & Signal Process., Anhui Univ., Hefei, China
Abstract :
In this paper, the major contribution is to combine Bayesian inference with iterative support detection (ISD) to solve noisy compressive sensing, which can be called Bayesian compressive sensing via iterative support detection (BCS_ISD). The method consists of two main parts: signal value estimation and signal support detection. ISD estimates a support set S from a current reconstruction and obtains a new reconstruction by MMSE estimator, and then it iterates these two steps for a small number of times. BCS_ISD converges fast and it reconstructs more exactly than other belief propagation (BP) approaches. Numerical experiments are provided to verify that BCS_ISD has significant advantages over those recent methods.
Keywords :
belief maintenance; compressed sensing; inference mechanisms; iterative methods; signal reconstruction; BCS_ISD; BP; Bayesian compressive sensing via iterative support detection; Bayesian inference; MMSE estimator; belief propagation approaches; iterative support detection; noisy compressive sensing; reconstruction approach; signal support detection; signal value estimation; Bayes methods; Compressed sensing; Estimation; Noise measurement; Signal to noise ratio; Sparse matrices; Bayesian inference; MMSE; compressive sensing; iterative support detection;
Conference_Titel :
Computer Science and Network Technology (ICCSNT), 2013 3rd International Conference on
Conference_Location :
Dalian
DOI :
10.1109/ICCSNT.2013.6967307