Iterative Forward-Backward Pursuit Algorithm for Compressed Sensing

• Published in: Journal of Electrical and Computer Engineering Vol. 2016; no. 2016; pp. 561 - 567
• Main Authors: Geng, Tianyu, Sun, Guiling, Zhang, Jianping, Wang, Feng
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Limiteds 01.01.2016; Hindawi Publishing Corporation; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Approximation; Computer simulation; Iterative methods; Iterative methods (Mathematics); Methods; NMR; Nuclear magnetic resonance; Performance enhancement; Performance evaluation; Reconstruction; Recovery; Signal processing; Sparsity; Strategy
• ISSN: 2090-0147; 2090-0155
• DOI: 10.1155/2016/5940371

It has been shown that iterative reweighted strategies will often improve the performance of many sparse reconstruction algorithms. Iterative Framework for Sparse Reconstruction Algorithms (IFSRA) is a recently proposed method which iteratively enhances the performance of any given arbitrary sparse reconstruction algorithm. However, IFSRA assumes that the sparsity level is known. Forward-Backward Pursuit (FBP) algorithm is an iterative approach where each iteration consists of consecutive forward and backward stages. Based on the IFSRA, this paper proposes the Iterative Forward-Backward Pursuit (IFBP) algorithm, which applies the iterative reweighted strategies to FBP without the need for the sparsity level. By using an approximate iteration strategy, IFBP gradually iterates to approach the unknown signal. Finally, this paper demonstrates that IFBP significantly improves the reconstruction capability of the FBP algorithm, via simulations including recovery of random sparse signals with different nonzero coefficient distributions in addition to the recovery of a sparse image.