TITLE:
Forward-Backward Synergistic Acceleration Pursuit Algorithm Based on Compressed Sensing
AUTHORS:
Bowen Zheng, Guiling Sun, Tianyu Geng, Weijian Zhao
KEYWORDS:
Compressed Sensing, Reconstruction Algorithm, Sparse Signal, FBP
JOURNAL NAME:
Journal of Computer and Communications,
Vol.5 No.10,
August
11,
2017
ABSTRACT: We propose the Forward-Backward Synergistic Acceleration Pursuit (FBSAP)
algorithm in this paper. The FBSAP algorithm inherits the advantages of the
Forward-Backward Pursuit (FBP) algorithm, which has high success rate of
reconstruction and does not necessitate the sparsity level as a priori condition.
Moreover, it solves the problem of FBP that the atom can be selected only by
the fixed step size. By mining the correlation between candidate atoms and residuals,
we innovatively propose the forward acceleration strategy to adjust the
forward step size adaptively and reduce the computation. Meanwhile, we accelerate
the algorithm further in backward step by fusing the strategy proposed
in Acceleration Forward-Backward Pursuit (AFBP) algorithm. The experimental
simulation results demonstrate that FBSAP can greatly reduce the running
time of the algorithm while guaranteeing the success rate in contrast to FBP
and AFBP.