Title :
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
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;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.241
