A Universal Sparse Signal Reconstruction Algorithm via Backtracking and Belief Propagation

Author

Fang Jiang ; Yanjun Hu ; Caiqing She

Author_Institution

Key Lab. of Intell. Comput. & Signal Process., Anhui Univ., Hefei, China

Volume

2

fYear

2014

Firstpage

550

Lastpage

554

Abstract

The belief propagation (BP) algorithm under the Bayesian framework can accelerate Compressed Sensing (CS) encoding and decoding by using the sparse encoder matrix. To improve the reconstruction performance we consider a backtracking-based belief propagation algorithm (CS-BBP) for the sparse signal reconstruction. The backtracking is added after performing BP and minimum mean square error (MMSE) estimate in every iteration. Simulation results show that the CS-BBP is a universal reconstruction algorithm which has a good performance for both 1-D Gaussian and 2-D image signal reconstructions.

Keywords

Bayes methods; Gaussian processes; backtracking; compressed sensing; image coding; image reconstruction; mean square error methods; message passing; sparse matrices; 1D Gaussian image signal reconstruction; 2D image signal reconstruction; BP algorithm; Bayesian framework; CS encoding; MMSE; backtracking; belief propagation; compressed sensing decoding; compressed sensing encoding; minimum mean square error estimation; sparse encoder matrix; universal sparse signal reconstruction algorithm; Bayes methods; Belief propagation; Compressed sensing; Image reconstruction; Signal processing algorithms; Signal reconstruction; Signal to noise ratio; Backtracking; Belief Propagation; Compressed Sensing; Support Detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on

Print_ISBN

978-1-4799-7004-9

Type

conf

DOI

10.1109/ISCID.2014.241

Filename

7082051