Sparsity estimation based adaptive matching pursuit algorithm.
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| Title: | Sparsity estimation based adaptive matching pursuit algorithm. |
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| 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] |
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| Database: | Engineering Source |
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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. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13807501 |
| DOI: | 10.1007/s11042-016-4295-0 |
