Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing
In this paper, an algorithm named stepwise subspace pursuit (SSP) is proposed for sparse signal recovery. Unlike existing algorithms that select support set from candidate sets directly, our approach eliminates useless information from the candidate through threshold processing at first and then rec...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/798537 |
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| _version_ | 1849683617147518976 |
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| author | ZheTao Li JingXiong Xie DengBiao Tu Young-June Choi |
| author_facet | ZheTao Li JingXiong Xie DengBiao Tu Young-June Choi |
| author_sort | ZheTao Li |
| collection | DOAJ |
| description | In this paper, an algorithm named stepwise subspace pursuit (SSP) is proposed for sparse signal recovery. Unlike existing algorithms that select support set from candidate sets directly, our approach eliminates useless information from the candidate through threshold processing at first and then recovers the signal through the largest correlation coefficients. We demonstrate that SSP significantly outperforms conventional techniques in recovering sparse signals whose nonzero values have exponentially decaying magnitudes or distribution of N ( 0,1 ) . Experimental results of Lena show that SSP is better than CoSaMP, OMP, and SP in terms of peak signal to noise ratio (PSNR) by 5.5 db, 4.1 db, and 4.2 db, respectively. |
| format | Article |
| id | doaj-art-d46f0471c4a745549bb22a8a3175ec4f |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-d46f0471c4a745549bb22a8a3175ec4f2025-08-20T03:23:47ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-08-01910.1155/2013/798537Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed SensingZheTao Li0JingXiong Xie1DengBiao Tu2Young-June Choi3 School of Computer, National University of Defense Technology, Hunan 410073, China The College of Information Engineering, Xiangtan University, Hunan 411105, China National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China Department of Information and Computer Engineering, Ajou University, Suwon 443749, Republic of KoreaIn this paper, an algorithm named stepwise subspace pursuit (SSP) is proposed for sparse signal recovery. Unlike existing algorithms that select support set from candidate sets directly, our approach eliminates useless information from the candidate through threshold processing at first and then recovers the signal through the largest correlation coefficients. We demonstrate that SSP significantly outperforms conventional techniques in recovering sparse signals whose nonzero values have exponentially decaying magnitudes or distribution of N ( 0,1 ) . Experimental results of Lena show that SSP is better than CoSaMP, OMP, and SP in terms of peak signal to noise ratio (PSNR) by 5.5 db, 4.1 db, and 4.2 db, respectively.https://doi.org/10.1155/2013/798537 |
| spellingShingle | ZheTao Li JingXiong Xie DengBiao Tu Young-June Choi Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing International Journal of Distributed Sensor Networks |
| title | Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing |
| title_full | Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing |
| title_fullStr | Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing |
| title_full_unstemmed | Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing |
| title_short | Sparse Signal Recovery by Stepwise Subspace Pursuit in Compressed Sensing |
| title_sort | sparse signal recovery by stepwise subspace pursuit in compressed sensing |
| url | https://doi.org/10.1155/2013/798537 |
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