Issue |
J. Eur. Opt. Soc.-Rapid Publ.
Volume 13, Number 1, 2017
|
|
---|---|---|
Article Number | 17 | |
Number of page(s) | 4 | |
DOI | https://doi.org/10.1186/s41476-017-0045-9 | |
Published online | 01 June 2017 |
Research
Sparse kronecker pascal measurement matrices for compressive imaging
College of Information and Communication Engineering, Harbin Engineering University, 150001, Harbin, China
Received:
20
March
2017
Accepted:
12
May
2017
Background: The construction of measurement matrix becomes a focus in compressed sensing (CS) theory. Although random matrices have been theoretically and practically shown to reconstruct signals, it is still necessary to study the more promising deterministic measurement matrix.
Methods: In this paper, a new method to construct a simple and efficient deterministic measurement matrix, sparse kronecker pascal (SKP) measurement matrix, is proposed, which is based on the kronecker product and the pascal matrix.
Results: Simulation results show that the reconstruction performance of the SKP measurement matrices is superior to that of the random Gaussian measurement matrices and random Bernoulli measurement matrices.
Conclusions: The SKP measurement matrix can be applied to reconstruct high-dimensional signals such as natural images. And the reconstruction performance of the SKP measurement matrix with a proper pascal matrix outperforms the random measurement matrices.
Key words: Compressed sensing / Deterministic measurement matrix / Kronecker product / Pascal matrix
© The Author(s) 2017
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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