13 May 2017 Fast sparsity adaptive multipath matching pursuit for compressed sensing problems
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Abstract
The high computational complexity of tree-based multipath search approaches makes putting them into practical use difficult. However, reselection of candidate atoms could make the search path more accurate and efficient. We propose a multipath greedy approach called fast sparsity adaptive multipath matching pursuit (fast SAMMP), which performs a sparsity adaptive tree search to find the sparsest solution with better performances. Each tree branch acquires K atoms, and fast SAMMP reselects the best K atoms among 2 K atoms. Fast SAMMP adopts sparsity adaptive techniques that allow more practical applications for the algorithm. We demonstrated the reconstruction performances of the proposed fast scheme on both synthetically generated one-dimensional signals and two-dimensional images using Gaussian observation matrices. The experimental results indicate that fast SAMMP achieves less reconstruction time and a much higher exact recovery ratio compared with conventional algorithms.
© 2017 SPIE and IS&T
Xiaofang Zhang, Xiaofang Zhang, Hongwei Du, Hongwei Du, Bensheng Qiu, Bensheng Qiu, Shanshan Chen, Shanshan Chen, } "Fast sparsity adaptive multipath matching pursuit for compressed sensing problems," Journal of Electronic Imaging 26(3), 033007 (13 May 2017). https://doi.org/10.1117/1.JEI.26.3.033007 . Submission: Received: 26 January 2017; Accepted: 26 April 2017
Received: 26 January 2017; Accepted: 26 April 2017; Published: 13 May 2017
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