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Sparsity estimation based adaptive matching pursuit algorithm.

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Title: Sparsity estimation based adaptive matching pursuit algorithm.
Authors: Yao, Shihong1 yao_shi_hong@whu.edu.cn, Wang, Tao2,3 wangtao.mac@whu.edu.cn, Chong, Yanwen2 apollobest@126.com, Pan, Shaoming2 pansm@whu.edu.cn
Source: Multimedia Tools & Applications. Feb2018, Vol. 77 Issue 4, p4095-4112. 18p.
Subjects: Image reconstruction algorithms, Sparse matrix software, Signal reconstruction, Restricted isometry property, Robust convex optimization
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. [ABSTRACT FROM AUTHOR]
Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Image+reconstruction+algorithms%22">Image reconstruction algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+matrix+software%22">Sparse matrix software</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+reconstruction%22">Signal reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Restricted+isometry+property%22">Restricted isometry property</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+convex+optimization%22">Robust convex optimization</searchLink>
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  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s11042-016-4295-0
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        Text: English
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      – SubjectFull: Image reconstruction algorithms
        Type: general
      – SubjectFull: Sparse matrix software
        Type: general
      – SubjectFull: Signal reconstruction
        Type: general
      – SubjectFull: Restricted isometry property
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      – SubjectFull: Robust convex optimization
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      – TitleFull: Sparsity estimation based adaptive matching pursuit algorithm.
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              Text: Feb2018
              Type: published
              Y: 2018
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