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Open AccessArticle

Measurement Matrix Construction for Large-area Single Photon Compressive Imaging

School of Information Engineering, Nanchang University, Nanchang 330031, China
State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 474;
Received: 11 December 2018 / Revised: 14 January 2019 / Accepted: 22 January 2019 / Published: 24 January 2019
(This article belongs to the Section Physical Sensors)
We have developed a single photon compressive imaging system based on single photon counting technology and compressed sensing theory, using a photomultiplier tube (PMT) photon counting head as the bucket detector. This system can realize ultra-weak light imaging with the imaging area up to the entire digital micromirror device (DMD) working region. The measurement matrix in this system is required to be binary due to the two working states of the micromirror corresponding to two controlled elements. And it has a great impact on the performance of the imaging system, because it involves modulation of the optical signal and image reconstruction. Three kinds of binary matrix including sparse binary random matrix, m sequence matrix and true random number matrix are constructed. The properties of these matrices are analyzed theoretically with the uncertainty principle. The parameters of measurement matrix including sparsity ratio, compressive sampling ratio and reconstruction time are verified in the experimental system. The experimental results show that, the increase of sparsity ratio and compressive sampling ratio can improve the reconstruction quality. However, when the increase is up to a certain value, the reconstruction quality tends to be saturated. Compared to the other two types of measurement matrices, the m sequence matrix has better performance in image reconstruction. View Full-Text
Keywords: single photon compressive imaging; compressed sensing; measurement matrix single photon compressive imaging; compressed sensing; measurement matrix
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Wang, H.; Yan, Q.; Li, B.; Yuan, C.; Wang, Y. Measurement Matrix Construction for Large-area Single Photon Compressive Imaging. Sensors 2019, 19, 474.

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