Principles and applications of high-speed single-pixel imaging technology

Abstract

Single-pixel imaging (SPI) technology has garnered great interestwithin the last decade because of its ability to record high-resolutionimages using a single-pixel detector. It has been applied to diversefields, such as magnetic resonance imaging (MRI), aerospace remotesensing, terahertz photography, and hyperspectral imaging. Comparedwith conventional silicon-based cameras, single-pixel cameras (SPCs)can achieve image compression and operate over a much broader spectralrange. However, the imaging speed of SPCs is governed by the responsetime of digital micromirror devices (DMDs) and the amount of compressionof acquired images, leading to low (ms-level) temporal resolution.Consequently, it is particularly challenging for SPCs to investigatefast dynamic phenomena, which is required commonly in microscopy.Recently, a unique approach based on photonic time stretch (PTS) toachieve high-speed SPI has been reported. It achieves a frame ratefar beyond that can be reached with conventional SPCs. In this paper,we first introduce the principles and applications of the PTS technique.Then the basic architecture of the high-speed SPI system is presented,and an imaging flow cytometer with high speed and high throughputis demonstrated experimentally. Finally, the limitations and potentialapplications of high-speed SPI are discussed.

Keywords

Compressive sampling / Single-pixelimaging / Photonic time stretch / Imaging flow cytometry

References

[1] Bioucas-Dias, J.M., Figueiredo, M.A.T., 2007. A new TwIST: two-step iterative shrinkage/thresholdingalgorithms for image restoration. IEEE Trans. Imag. Process., 16(12): 2992–3004. https://doi.org/10.1109/TIP.2007.909319

[2] Blumensath, T., Davies, M.E., 2009. Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal., 27(3):265–274. https://doi.org/10.1016/j.acha.2009.04.002

[3] Bosworth, B.T., Foster, M.A., 2014. High-speed flow imaging utilizing spectral-encoding ofultrafast pulses and compressed sensing. OSA Techn. Dig., Paper ATh4P.3. https://doi.org/10.1364/CLEO_AT.2014.ATh4P.3

[4] Bosworth, B.T., Stroud, J.R., Tran, D.N., , 2015. High-speed flow microscopy using compressed sensing with ultrafastlaser pulses. Opt.Expr., 23(8):10521–10532. https://doi.org/10.1364/OE.23.010521

[5] Candès, E.J., Wakin, M.B., 2008. An introduction to compressive sampling. IEEE Signal Process. Mag., 25(2):21–30. https://doi.org/10.1109/MSP.2007.914731

[6] Candès, E.J., Romberg, J., Tao, T., 2006. Robustuncertainty principles: exact signal reconstruction from highly incompletefrequency information. IEEE Trans. Inform. Theory, 52(2):489–509. https://doi.org/10.1109/TIT.2005.862083

[7] Chan, A.C.S., Lau, A.K.S., Wong, K.K.Y., , 2015. Arbitrary two-dimensional spectrally encoded pattern generation—anew strategy for high-speed patterned illumination imaging.Optica,2(12):1037-1044. https://doi.org/10.1364/OPTICA.2.001037

[8] Chen, C.L.F., Mahjoubfar, A., Jalali, B., 2015. Opticaldata compression in time stretch imaging. PLOS ONE, 10(4): 0125106. https://doi.org/10.1371/journal.pone.0125106

[9] Donoho, D.L., 2006. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289–1306. https://doi.org/10.1109/TIT.2006.871582

[10] Duarte, M.F., Davenport, M.A., Takhar, D., , 2008. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag., 25(2):83–91. https://doi.org/10.1109/MSP.2007.914730

[11] Figueiredo, M.A.T., Nowak, R.D., Wright, S.J., 2007. Gradientprojection for sparse reconstruction: application to compressed sensingand other inverse problems. IEEE J. Sel. Topics Signal Process., 1(4):586–597. https://doi.org/10.1109/JSTSP.2007.910281

[12] Goda, K., Jalali, B., 2013. Dispersive Fourier transformation for fast continuoussingle-shot measurements. Nat. Photon., 7:102–112. https://doi.org/10.1038/nphoton.2012.359

[13] Goda, K., Tsia, K.K., Jalali, B., 2009. Serial time-encodedamplified imaging for real-time observation of fast dynamic phenomena. Nature, 458:1145–1149. https://doi.org/10.1038/nature07980

[14] Goda, K., Ayazi, A., Gossett, D.R., , 2012. Highthroughput single-microparticle imaging flow analyzer. PNAS, 109(29):11630–11635. https://doi.org/10.1073/pnas.1204718109

[15] Guo, Q., Chen, H.W., Weng, Z.L., , 2015. Fast time-lensbasedline-scan single-pixel camera with multiwavelength source. Biomed. Opt. Expr., 6(9):3610–3617. https://doi.org/10.1364/BOE.6.003610

[16] Lau, A.K., Shum, H.C., Wong, K.K., , 2016. Optofluidictime-stretch imaging—an emerging tool for highthroughput imagingflow cytometry. Lab Chip, 16(10): 1743–1756. https://doi.org/10.1039/C5LC01458A

[17] Lei, C., Guo, B., Cheng, Z., , 2016. Optical time-stretchimaging: principles and applications. Appl. Phys. Rev., 3(1):011102. https://doi.org/10.1063/1.4941050

[18] Lustig, M., Donoho, D., Pauly, J.M., 2007. SparseMRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med., 58(6):1182–1195. https://doi.org/10.1002/mrm.21391

[19] Needell, D., Tropp, J.A., 2009. CoSaMP: iterative signal recovery from incomplete andinaccurate samples. Appl. Comput. Harmon. Anal., 26(3):301–321. https://doi.org/10.1016/j.acha.2008.07.002

[20] Takhar, D., Laska, J., Wakin, M.B., , 2006. A new compressiveimaging camera architecture using opticaldomain compression. SPIE, 6065:43–52. https://doi.org/10.1117/12.659602

[21] Tropp, J.A., Gilbert, A.C., 2007. Signal recovery from random measurements via orthogonalmatching pursuit. IEEE Trans. Inform. Theory, 53(12):4655–4666. https://doi.org/10.1109/TIT.2007.909108

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