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

  • Qiang Guo
  • Yu-xi Wang
  • Hong-wei Chen
  • Ming-hua Chen
  • Si-gang Yang
  • Shi-zhong Xie


Single-pixel imaging (SPI) technology has garnered great interest within the last decade because of its ability to record high-resolution images using a single-pixel detector. It has been applied to diverse fields, such as magnetic resonance imaging (MRI), aerospace remote sensing, terahertz photography, and hyperspectral imaging. Compared with conventional silicon-based cameras, single-pixel cameras (SPCs) can achieve image compression and operate over a much broader spectral range. However, the imaging speed of SPCs is governed by the response time of digital micromirror devices (DMDs) and the amount of compression of acquired images, leading to low (ms-level) temporal resolution. Consequently, it is particularly challenging for SPCs to investigate fast dynamic phenomena, which is required commonly in microscopy. Recently, a unique approach based on photonic time stretch (PTS) to achieve high-speed SPI has been reported. It achieves a frame rate far 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 throughput is demonstrated experimentally. Finally, the limitations and potential applications of high-speed SPI are discussed.

Key words

Compressive sampling Single-pixel imaging Photonic time stretch Imaging flow cytometry 

CLC number



  1. Bioucas-Dias, J.M., Figueiredo, M.A.T., 2007. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans. Imag. Process., 16(12): 2992–3004. Scholar
  2. Blumensath, T., Davies, M.E., 2009. Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal., 27(3):265–274. Scholar
  3. Bosworth, B.T., Foster, M.A., 2014. High-speed flow imaging utilizing spectral-encoding of ultrafast pulses and compressed sensing. OSA Techn. Dig., Paper ATh4P.3. Scholar
  4. Bosworth, B.T., Stroud, J.R., Tran, D.N., et al., 2015. High-speed flow microscopy using compressed sensing with ultrafast laser pulses. Opt. Expr., 23(8):10521–10532. Scholar
  5. Candès, E.J., Wakin, M.B., 2008. An introduction to compressive sampling. IEEE Signal Process. Mag., 25(2):21–30. Scholar
  6. Candès, E.J., Romberg, J., Tao, T., 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory, 52(2):489–509. Scholar
  7. Chan, A.C.S., Lau, A.K.S., Wong, K.K.Y., et al., 2015. Arbitrary two-dimensional spectrally encoded pattern generation—a new strategy for high-speed patterned illumination imaging. Optica, 2(12):1037–1044. Scholar
  8. Chen, C.L.F., Mahjoubfar, A., Jalali, B., 2015. Optical data compression in time stretch imaging. PLOS ONE, 10(4): 0125106. Scholar
  9. Donoho, D.L., 2006. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289–1306. Scholar
  10. Duarte, M.F., Davenport, M.A., Takhar, D., et al., 2008. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag., 25(2):83–91. Scholar
  11. Figueiredo, M.A.T., Nowak, R.D., Wright, S.J., 2007. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J. Sel. Topics Signal Process., 1(4):586–597. Scholar
  12. Goda, K., Jalali, B., 2013. Dispersive Fourier transformation for fast continuous single-shot measurements. Nat. Photon., 7:102–112. Scholar
  13. Goda, K., Tsia, K.K., Jalali, B., 2009. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena. Nature, 458:1145–1149. Scholar
  14. Goda, K., Ayazi, A., Gossett, D.R., et al., 2012. Highthroughput single-microparticle imaging flow analyzer. PNAS, 109(29):11630–11635. Scholar
  15. Guo, Q., Chen, H.W., Weng, Z.L., et al., 2015. Fast time-lensbased line-scan single-pixel camera with multiwavelength source. Biomed. Opt. Expr., 6(9):3610–3617. Scholar
  16. Lau, A.K., Shum, H.C., Wong, K.K., et al., 2016. Optofluidic time-stretch imaging—an emerging tool for highthroughput imaging flow cytometry. Lab Chip, 16(10): 1743–1756. Scholar
  17. Lei, C., Guo, B., Cheng, Z., et al., 2016. Optical time-stretch imaging: principles and applications. Appl. Phys. Rev., 3(1):011102. Scholar
  18. Lustig, M., Donoho, D., Pauly, J.M., 2007. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med., 58(6):1182–1195. Scholar
  19. Needell, D., Tropp, J.A., 2009. CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal., 26(3):301–321. Scholar
  20. Takhar, D., Laska, J., Wakin, M.B., et al., 2006. A new compressive imaging camera architecture using opticaldomain compression. SPIE, 6065:43–52. Scholar
  21. Tropp, J.A., Gilbert, A.C., 2007. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory, 53(12):4655–4666. Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Tsinghua National Laboratory for Information Science and Technology, Department of Electronic EngineeringTsinghua UniversityBeijingChina

Personalised recommendations