Abstract

Single-pixel imaging (SPI) has recently been intensively studied as an alternative to the traditional focal plane array (FPA) technology. However, limited by the refresh rate of spatial light modulators (SLM) and inherent reconstruction mechanism, SPI is inappropriate for high-speed moving targets. To break through this limitation, we propose a novel SPI scheme for high-speed moving targets. In our scenario, the spatial encoding for the target is done by the movement of the target relative to a static pseudo-random illumination pattern. In this process, a series of single-pixel signals are generated that corresponds to the overlap between the target and certain parts of the illumination structure. This correspondence can be utilized for image reconstruction in the same way as normal SPI. In addition, compressive sensing and deep learning algorithms are used for reconstruction, respectively. Reasonable reconstructions can be obtained with a sampling ratio of only 6%. Experimental verification together with theoretical analysis has shown that our scheme is able to image high-speed moving targets that could be alternatively achieved by a fast FPA camera. Our scheme keeps the inherent advantages of SPI and meanwhile extend its application to moving targets. It is believed that this technology will have wide application in many situations.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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2019 (2)

2018 (4)

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).
[Crossref]

Z.-H. Xu, W. Chen, J. Penuelas, M. Padgett, and M.-J. Sun, “1000 fps computational ghost imaging using led-based structured illumination,” Opt. Express 26(3), 2427–2434 (2018).
[Crossref]

A.-X. Zhang, Y.-H. He, L.-A. Wu, L.-M. Chen, and B.-B. Wang, “Tabletop x-ray ghost imaging with ultra-low radiation,” Optica 5(4), 374–377 (2018).
[Crossref]

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

2017 (2)

2016 (1)

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

2015 (1)

2014 (1)

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516(7529), 74–77 (2014).
[Crossref]

2013 (1)

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. Padgett, “3d computational imaging with single-pixel detectors,” Science 340(6134), 844–847 (2013).
[Crossref]

2009 (3)

X.-H. Chen, Q. Liu, K.-H. Luo, and L.-A. Wu, “Lensless ghost imaging with true thermal light,” Opt. Lett. 34(5), 695–697 (2009).
[Crossref]

Y. Bromberg, O. Katz, and Y. Silberberg, “Ghost imaging with a single detector,” Phys. Rev. A 79(5), 053840 (2009).
[Crossref]

O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95(13), 131110 (2009).
[Crossref]

2008 (2)

J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78(6), 061802 (2008).
[Crossref]

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

2004 (1)

N. Nebeker, “Ccds outperform film in rotating-mirror cameras,” Photonics Spectra 38, 116–118 (2004).

1995 (1)

T. Pittman, Y. Shih, D. Strekalov, and A. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
[Crossref]

1878 (1)

O. Munn and A. Beach, “A horse’s motion scientifically determined,” Sci. Am. 39(16), 241 (1878).
[Crossref]

Baraniuk, R. G.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

A. Mousavi and R. G. Baraniuk, “Learning to invert: Signal recovery via deep convolutional networks,” in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), (IEEE, 2017), pp. 2272–2276.

Beach, A.

O. Munn and A. Beach, “A horse’s motion scientifically determined,” Sci. Am. 39(16), 241 (1878).
[Crossref]

Bowman, A.

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. Padgett, “3d computational imaging with single-pixel detectors,” Science 340(6134), 844–847 (2013).
[Crossref]

Bowman, R.

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. Padgett, “3d computational imaging with single-pixel detectors,” Science 340(6134), 844–847 (2013).
[Crossref]

Bromberg, Y.

O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95(13), 131110 (2009).
[Crossref]

Y. Bromberg, O. Katz, and Y. Silberberg, “Ghost imaging with a single detector,” Phys. Rev. A 79(5), 053840 (2009).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.

Candes, E.

E. Candes and J. Romberg, “l1-magic: Recovery of sparse signals via convex programming,” URL: www.acm.caltech.edu/l1magic/downloads/l1magic.pdf 4, 14 (2005).

Chen, L.-M.

Chen, W.

Chen, X.-H.

Cossairt, O.

Dai, Q.

Y. Wang, Y. Liu, J. Suo, G. Situ, C. Qiao, and Q. Dai, “High speed computational ghost imaging via spatial sweeping,” Sci. Rep. 7(1), 45325 (2017).
[Crossref]

Davenport, M. A.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Duarte, M. F.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Edgar, M. P.

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).
[Crossref]

G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25(4), 2998–3005 (2017).
[Crossref]

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. Padgett, “3d computational imaging with single-pixel detectors,” Science 340(6134), 844–847 (2013).
[Crossref]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.

Fujiu, K.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Gao, L.

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516(7529), 74–77 (2014).
[Crossref]

Gibson, G. M.

G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25(4), 2998–3005 (2017).
[Crossref]

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

Gu, J.

Hashimoto, K.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

He, G.

He, Y.-H.

Hempler, N.

Hendry, E.

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

Higham, C. F.

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).
[Crossref]

Hobson, P. A.

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

Horisaki, R.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Hornett, S. M.

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

Jiang, S.

Jiang, W.

Kamesawa, R.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Katsaggelos, A. K.

Katz, O.

Y. Bromberg, O. Katz, and Y. Silberberg, “Ghost imaging with a single detector,” Phys. Rev. A 79(5), 053840 (2009).
[Crossref]

O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95(13), 131110 (2009).
[Crossref]

Kawamura, Y.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Kelly, K. F.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Koller, R.

Laska, J. N.

M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
[Crossref]

Li, C.

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516(7529), 74–77 (2014).
[Crossref]

Li, X.

Liang, J.

L. Gao, J. Liang, C. Li, and L. V. Wang, “Single-shot compressed ultrafast photography at one hundred billion frames per second,” Nature 516(7529), 74–77 (2014).
[Crossref]

Lin, H.

Liu, Q.

Liu, W.

Liu, Y.

Y. Wang, Y. Liu, J. Suo, G. Situ, C. Qiao, and Q. Dai, “High speed computational ghost imaging via spatial sweeping,” Sci. Rep. 7(1), 45325 (2017).
[Crossref]

Luo, K.-H.

Mach, E.

E. Mach and P. Salcher, Photographische Fixirung der durch Projectile in der Luft eingeleiteten Vorgänge (K. k. Hof-u. Staatsdruckerei, 1887).

Maker, G. T.

Malcolm, G. P.

Matsuda, N.

Mousavi, A.

A. Mousavi and R. G. Baraniuk, “Learning to invert: Signal recovery via deep convolutional networks,” in 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP), (IEEE, 2017), pp. 2272–2276.

Munn, O.

O. Munn and A. Beach, “A horse’s motion scientifically determined,” Sci. Am. 39(16), 241 (1878).
[Crossref]

Murray-Smith, R.

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).
[Crossref]

Nebeker, N.

N. Nebeker, “Ccds outperform film in rotating-mirror cameras,” Photonics Spectra 38, 116–118 (2004).

Niederberger, T.

Noji, H.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Ota, S.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Padgett, M.

Z.-H. Xu, W. Chen, J. Penuelas, M. Padgett, and M.-J. Sun, “1000 fps computational ghost imaging using led-based structured illumination,” Opt. Express 26(3), 2427–2434 (2018).
[Crossref]

B. Sun, M. P. Edgar, R. Bowman, L. E. Vittert, S. Welsh, A. Bowman, and M. Padgett, “3d computational imaging with single-pixel detectors,” Science 340(6134), 844–847 (2013).
[Crossref]

Padgett, M. J.

C. F. Higham, R. Murray-Smith, M. J. Padgett, and M. P. Edgar, “Deep learning for real-time single-pixel video,” Sci. Rep. 8(1), 2369 (2018).
[Crossref]

G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25(4), 2998–3005 (2017).
[Crossref]

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

Penuelas, J.

Phillips, D. B.

Photonics, H.

H. Photonics, “Guide to streak cameras,” Hamamatsu City, Japan (2008).

Pittman, T.

T. Pittman, Y. Shih, D. Strekalov, and A. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
[Crossref]

Qiao, C.

Y. Wang, Y. Liu, J. Suo, G. Situ, C. Qiao, and Q. Dai, “High speed computational ghost imaging via spatial sweeping,” Sci. Rep. 7(1), 45325 (2017).
[Crossref]

Romberg, J.

E. Candes and J. Romberg, “l1-magic: Recovery of sparse signals via convex programming,” URL: www.acm.caltech.edu/l1magic/downloads/l1magic.pdf 4, 14 (2005).

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention, (Springer, 2015), pp. 234–241.

Salcher, P.

E. Mach and P. Salcher, Photographische Fixirung der durch Projectile in der Luft eingeleiteten Vorgänge (K. k. Hof-u. Staatsdruckerei, 1887).

Sato, I.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Schmid, L.

Schuster, G.

Sergienko, A.

T. Pittman, Y. Shih, D. Strekalov, and A. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
[Crossref]

Setoyama, K.

S. Ota, R. Horisaki, Y. Kawamura, M. Ugawa, I. Sato, K. Hashimoto, R. Kamesawa, K. Setoyama, S. Yamaguchi, K. Fujiu, K. Waki, and H. Noji, “Ghost cytometry,” Science 360(6394), 1246–1251 (2018).
[Crossref]

Shapiro, J. H.

J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78(6), 061802 (2008).
[Crossref]

Shih, Y.

T. Pittman, Y. Shih, D. Strekalov, and A. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
[Crossref]

Silberberg, Y.

O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95(13), 131110 (2009).
[Crossref]

Y. Bromberg, O. Katz, and Y. Silberberg, “Ghost imaging with a single detector,” Phys. Rev. A 79(5), 053840 (2009).
[Crossref]

Situ, G.

Y. Wang, Y. Liu, J. Suo, G. Situ, C. Qiao, and Q. Dai, “High speed computational ghost imaging via spatial sweeping,” Sci. Rep. 7(1), 45325 (2017).
[Crossref]

Spinoulas, L.

Stantchev, R. I.

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

Strekalov, D.

T. Pittman, Y. Shih, D. Strekalov, and A. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
[Crossref]

Sun, B.

S. Jiang, X. Li, Z. Zhang, W. Jiang, Y. Wang, G. He, Y. Wang, and B. Sun, “Scan efficiency of structured illumination in iterative single pixel imaging,” Opt. Express 27(16), 22499–22507 (2019).
[Crossref]

G. M. Gibson, B. Sun, M. P. Edgar, D. B. Phillips, N. Hempler, G. T. Maker, G. P. Malcolm, and M. J. Padgett, “Real-time imaging of methane gas leaks using a single-pixel camera,” Opt. Express 25(4), 2998–3005 (2017).
[Crossref]

R. I. Stantchev, B. Sun, S. M. Hornett, P. A. Hobson, G. M. Gibson, M. J. Padgett, and E. Hendry, “Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector,” Sci. Adv. 2(6), e1600190 (2016).
[Crossref]

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Figures (6)

Fig. 1.
Fig. 1. Schematic diagram of the proposed scheme. The target with a constant velocity $v$ moves along the longer side of the static pseudo-random illumination pattern. Meanwhile, the intensity $g(t)$ of reflected light from the target is recorded by a single pixel detector. $K$ different sub-patterns are divided from the movement of the target throughout the illumination pattern. The weights of each sub-pattern can be mined from the signal $g(t)$. Then, the image of the moving target is reconstructed in a normal SPI method.
Fig. 2.
Fig. 2. Schematic diagram of the experimental setup. LED light is directed onto the DMD by a reflecting mirror. A camera lens is used to project the DMD pattern onto the target area. A single pixel detector is used with a ADC to record the intensity of the reflected light from the target.
Fig. 3.
Fig. 3. Comparison of traditional SPI for stationary targets and proposed SPI for moving targets. (a1) The reconstruction results of static target by traditional SPI. (a2)-(a5) Reconstruction results of the moving target using proposed scheme at a speed of 17$mm/s$ with 28% sampling ratio, when the detection sampling rate is (a2) 3KHz, (a3) 1KHz, (a4) 200Hz and (a5) 100Hz respectively. Correspondingly, the number of detection samples recorded from the target under one illumination sub-pattern is 30, 10 , 2 and 1 respectively. (b) The PSNR and SSIM curves of reconstructed image with different detection samples under one illumination sub-pattern.
Fig. 4.
Fig. 4. Comparison of experimental results at different sampling ratio. (a) Reconstruction results at 16%, 22% and 28% sampling ratio for target moving at 17$mm/s$ and $34mm/s$, respectively. (b) The PSNR and SSIM curves of reconstructed images with different sampling ratio at 17$mm/s$ and $34mm/s$, respectively.
Fig. 5.
Fig. 5. Framework of our proposed scheme using deep neural networks.
Fig. 6.
Fig. 6. Reconstruction results using compressive sensing and deep learning under 6% and 10% sampling ratio, when the moving velocity of the target is 17$mm/s$ and 34$mm/s$ respectively.

Equations (7)

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g i = P i ( x , y ) T ( x , y ) d x d y
T ^ ( x , y ) = 1 n i = 1 n ( g i g ) P i ( x , y )
g ( t ) = P ( : , v t : v t + A ) T
W 2 v d f a f
y = Φ x
x ^ = a r g   m i n 1 2 y Φ x 2 2 + λ Ψ ( x )
T V ( x ) = [ D h ( x ) ] 2 + [ D v ( x ) ] 2

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