Full Text:   <2527>

Summary:  <1803>

CLC number: TN911.73

On-line Access: 2017-10-25

Received: 2016-11-18

Revision Accepted: 2017-04-17

Crosschecked: 2017-09-15

Cited: 1

Clicked: 6320

Citations:  Bibtex RefMan EndNote GB/T7714


Hong-wei Chen


-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.9 P.1261-1267


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

Author(s):  Qiang Guo, Yu-xi Wang, Hong-wei Chen, Ming-hua Chen, Si-gang Yang, Shi-zhong Xie

Affiliation(s):  Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Corresponding email(s):   q-guo13@mails.tsinghua.edu.cn, auvr123@163.com, chenhw@mail.tsinghua.edu.cn, chenmh@tsinghua.edu.cn, ysg@tsinghua.edu.cn, xsz-dee@mail.tsinghua.edu.cn

Key Words:  Compressive sampling, Single-pixel imaging, Photonic time stretch, Imaging flow cytometry

Qiang Guo, Yu-xi Wang, Hong-wei Chen, Ming-hua Chen, Si-gang Yang, Shi-zhong Xie. Principles and applications of high-speed single-pixel imaging technology[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(9): 1261-1267.

@article{title="Principles and applications of high-speed single-pixel imaging technology",
author="Qiang Guo, Yu-xi Wang, Hong-wei Chen, Ming-hua Chen, Si-gang Yang, Shi-zhong Xie",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Principles and applications of high-speed single-pixel imaging technology
%A Qiang Guo
%A Yu-xi Wang
%A Hong-wei Chen
%A Ming-hua Chen
%A Si-gang Yang
%A Shi-zhong Xie
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 9
%P 1261-1267
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601719

T1 - Principles and applications of high-speed single-pixel imaging technology
A1 - Qiang Guo
A1 - Yu-xi Wang
A1 - Hong-wei Chen
A1 - Ming-hua Chen
A1 - Si-gang Yang
A1 - Shi-zhong Xie
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 9
SP - 1261
EP - 1267
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601719

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[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.

[2]Blumensath, T., Davies, M.E., 2009. Iterative hard thresholding for compressed sensing. Appl. Comput. Harmon. Anal., 27(3):265-274.

[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.

[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.

[5]Candès, E.J., Wakin, M.B., 2008. An introduction to compressive sampling. IEEE Signal Process. Mag., 25(2): 21-30.

[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.

[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.

[8]Chen, C.L.F., Mahjoubfar, A., Jalali, B., 2015. Optical data compression in time stretch imaging. PLOS ONE, 10(4): 0125106.

[9]Donoho, D.L., 2006. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289-1306.

[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.

[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.

[12]Goda, K., Jalali, B., 2013. Dispersive Fourier transformation for fast continuous single-shot measurements. Nat. Photon., 7:102-112.

[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.

[14]Goda, K., Ayazi, A., Gossett, D.R., et al., 2012. High-throughput single-microparticle imaging flow analyzer. PNAS, 109(29):11630-11635.

[15]Guo, Q., Chen, H.W., Weng, Z.L., et al., 2015. Fast time-lens- based line-scan single-pixel camera with multi-wavelength source. Biomed. Opt. Expr., 6(9):3610-3617.

[16]Lau, A.K., Shum, H.C., Wong, K.K., et al., 2016. Optofluidic time-stretch imaging—an emerging tool for high-throughput imaging flow cytometry. Lab Chip, 16(10): 1743-1756.

[17]Lei, C., Guo, B., Cheng, Z., et al., 2016. Optical time-stretch imaging: principles and applications. Appl. Phys. Rev., 3(1):011102.

[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.

[19]Needell, D., Tropp, J.A., 2009. CoSaMP: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal., 26(3):301-321.

[20]Takhar, D., Laska, J., Wakin, M.B., et al., 2006. A new compressive imaging camera architecture using optical-domain compression. SPIE, 6065:43-52.

[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.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE