Fast compressive measurements acquisition using optimized binary sensing matrices for low-light-level imaging

Optics Express
Jun Ke, Edmund Y Lam

Abstract

Compressive measurements benefit low-light-level imaging (L<sup>3</sup>-imaging) due to the significantly improved measurement signal-to-noise ratio (SNR). However, as with other compressive imaging (CI) systems, compressive L<sup>3</sup>-imaging is slow. To accelerate the data acquisition, we develop an algorithm to compute the optimal binary sensing matrix that can minimize the image reconstruction error. First, we make use of the measurement SNR and the reconstruction mean square error (MSE) to define the optimal gray-value sensing matrix. Then, we construct an equality-constrained optimization problem to solve for a binary sensing matrix. From several experimental results, we show that the latter delivers a similar reconstruction performance as the former, while having a smaller dynamic range requirement to system sensors.

References

Aug 7, 2007·Applied Optics·Mark A Neifeld, Jun Ke
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Jul 3, 2010·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Zhimin Xu, Edmund Y Lam
May 23, 2012·Applied Optics·Yuval KashterAdrian Stern
Oct 6, 2012·Optics Express·Jun Ke, Edmund Y Lam
May 23, 2013·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Amit AshokMark A Neifeld
Oct 10, 2013·Optics Express·Gregory A HowlandJohn C Howell
Apr 9, 2014·IEEE Transactions on Medical Imaging·Jaya PrakashPhaneendra K Yalavarthy
Oct 17, 2014·Applied Optics·Abhijit Mahalanobis, Mark Neifeld
May 15, 2015·Applied Optics·M E Gehm, D J Brady

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Citations

Mar 24, 2018·Scientific Reports·Xialin LiuGuihua Zeng
Apr 1, 2020·Optics Express·Xialin LiuGuihua Zeng
Nov 22, 2018·Journal of the Optical Society of America. A, Optics, Image Science, and Vision·Xialin LiuGuihua Zeng
Mar 18, 2021·Optics Express·Jun KeEdmund Y Lam

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