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https://doi.org/10.1109/JSTSP.2009.2039176

has been cited by the following article:

• TITLE: Improvement of Forward-Backward Pursuit Algorithm Based on Weak Selection

  AUTHORS: Guiling Sun, Yingying Zhang, Jun Jia

  KEYWORDS: Compressed Sensing, Reconstruction Algorithm, FBP, Weak Selection

  JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.1, December 30, 2016

  ABSTRACT: Forward-backward pursuit (FBP) algorithm is a novel two-stage greedy approach. However once its forward and backward steps were determined during iteration, it would make computing time increased and affected the reconstruction efficiency. This paper presents a algorithm called forward-backward pursuit algorithm based on weak selection (SWFBP) by introducing threshold strategy into FBP algorithm, and in view of that in the first few iterations, most of the atoms which are selected are right, so this part of atoms are directly incorporated into support set instead of using backward strategy to reduce them. Flexible forward and backward steps accelerate the speed of atom selecting and improve the reconstruction accuracy. We compared SWFBP and FBP algorithm via one-dimensional signal and two-dimensional image reconstruction experiments. The simulation results demonstrate that compared with FBP, SWFBP algorithm has superior performance, including higher PSNR, faster computing speed and lower recovery time.