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Sparsity estimation based adaptive matching pursuit algorithm

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Abstract

Compared with convex optimization algorithms and combination algorithms, greedy pursuit algorithms can balance operational efficiency and reconstruction precision, so they are widely used in the signal reconstruction step of compressed sensing. However, most existing greedy pursuit algorithms only work well if the signal sparsity is known, and their reconstruction performance is influenced by signal sparsity. To more accurately match the sparsity and obtain better reconstruction performance, we propose a greedy pursuit algorithm, the sparsity estimation based adaptive matching pursuit algorithm, which achieves image reconstruction using a signal sparsity estimation based on the Restricted Isometry Property (RIP) criterion and a flexible step size. Experimental results demonstrate that this algorithm provides better reconstruction performance and lower computation time, using different measurement matrices, when the sparsity is estimated in advance.

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Acknowledgements

The research work reported in this paper is supported by the National Natural Science Foundation of China (No: 41271398, 41671382, 61572372), China Postdoctoral Science Foundation (Grant No.2016 M592409), National Program on Key Basic Research Project (No: 2011CB302306). In addition, this work is partially supported by LIESMARS Special Research Funding, SAST Funding (No. SAST201425) and Open Funding of NUIST, PAPD and CICAEET.

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Authors and Affiliations

  1. Faculty of information engineering, China University of Geosciences, Wuhan, China

    Shihong Yao

  2. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

    Tao Wang, Yanwen Chong & Shaoming Pan

  3. Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan, China

    Tao Wang

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  1. Shihong Yao
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  2. Tao Wang
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  3. Yanwen Chong
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  4. Shaoming Pan
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Correspondence to Yanwen Chong.

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Yao, S., Wang, T., Chong, Y. et al. Sparsity estimation based adaptive matching pursuit algorithm. Multimed Tools Appl 77, 4095–4112 (2018). https://doi.org/10.1007/s11042-016-4295-0

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  • DOI: https://doi.org/10.1007/s11042-016-4295-0

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