Deterministic Sensing Matrices in Compressive Sensing: A Survey
Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing...
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| Format: | Online |
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Hindawi Publishing Corporation
2013
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836388/ |
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pubmed-38363882013-12-12 Deterministic Sensing Matrices in Compressive Sensing: A Survey Nguyen, Thu L. N. Shin, Yoan Research Article Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered. These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this paper, a survey of deterministic sensing matrices for compressive sensing is presented. We introduce a basic problem in compressive sensing and some disadvantage of the random sensing matrices. Some recent results on construction of the deterministic sensing matrices are discussed. Hindawi Publishing Corporation 2013-11-05 /pmc/articles/PMC3836388/ /pubmed/24348141 http://dx.doi.org/10.1155/2013/192795 Text en Copyright © 2013 T. L. N. Nguyen and Y. Shin. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Nguyen, Thu L. N. Shin, Yoan |
| spellingShingle |
Nguyen, Thu L. N. Shin, Yoan Deterministic Sensing Matrices in Compressive Sensing: A Survey |
| author_facet |
Nguyen, Thu L. N. Shin, Yoan |
| author_sort |
Nguyen, Thu L. N. |
| title |
Deterministic Sensing Matrices in Compressive Sensing: A Survey |
| title_short |
Deterministic Sensing Matrices in Compressive Sensing: A Survey |
| title_full |
Deterministic Sensing Matrices in Compressive Sensing: A Survey |
| title_fullStr |
Deterministic Sensing Matrices in Compressive Sensing: A Survey |
| title_full_unstemmed |
Deterministic Sensing Matrices in Compressive Sensing: A Survey |
| title_sort |
deterministic sensing matrices in compressive sensing: a survey |
| description |
Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements. One of the most concerns in compressive sensing is the construction of the sensing matrices. While random sensing matrices have been widely studied, only a few deterministic sensing matrices have been considered. These matrices are highly desirable on structure which allows fast implementation with reduced storage requirements. In this paper, a survey of deterministic sensing matrices for compressive sensing is presented. We introduce a basic problem in compressive sensing and some disadvantage of the random sensing matrices. Some recent results on construction of the deterministic sensing matrices are discussed. |
| publisher |
Hindawi Publishing Corporation |
| publishDate |
2013 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3836388/ |
| _version_ |
1612029632863797248 |