The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing
This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse si...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/6950819 |
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| _version_ | 1832556401186045952 |
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| author | Yangyang Li Jianping Zhang Guiling Sun Dongxue Lu |
| author_facet | Yangyang Li Jianping Zhang Guiling Sun Dongxue Lu |
| author_sort | Yangyang Li |
| collection | DOAJ |
| description | This paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. The proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms. |
| format | Article |
| id | doaj-art-a6a8b21588bd4283a0e978e043cd76aa |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-a6a8b21588bd4283a0e978e043cd76aa2025-02-03T05:45:32ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552019-01-01201910.1155/2019/69508196950819The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed SensingYangyang Li0Jianping Zhang1Guiling Sun2Dongxue Lu3College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaElectrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USACollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaCollege of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, ChinaThis paper proposes a novel sparsity adaptive simulated annealing algorithm to solve the issue of sparse recovery. This algorithm combines the advantage of the sparsity adaptive matching pursuit (SAMP) algorithm and the simulated annealing method in global searching for the recovery of the sparse signal. First, we calculate the sparsity and the initial support collection as the initial search points of the proposed optimization algorithm by using the idea of SAMP. Then, we design a two-cycle reconstruction method to find the support sets efficiently and accurately by updating the optimization direction. Finally, we take advantage of the sparsity adaptive simulated annealing algorithm in global optimization to guide the sparse reconstruction. The proposed sparsity adaptive greedy pursuit model has a simple geometric structure, it can get the global optimal solution, and it is better than the greedy algorithm in terms of recovery quality. Our experimental results validate that the proposed algorithm outperforms existing state-of-the-art sparse reconstruction algorithms.http://dx.doi.org/10.1155/2019/6950819 |
| spellingShingle | Yangyang Li Jianping Zhang Guiling Sun Dongxue Lu The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing Journal of Electrical and Computer Engineering |
| title | The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing |
| title_full | The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing |
| title_fullStr | The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing |
| title_full_unstemmed | The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing |
| title_short | The Sparsity Adaptive Reconstruction Algorithm Based on Simulated Annealing for Compressed Sensing |
| title_sort | sparsity adaptive reconstruction algorithm based on simulated annealing for compressed sensing |
| url | http://dx.doi.org/10.1155/2019/6950819 |
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