Abstract

Sampling and reconstruction techniques are of special interest and importance in ghost imaging. Up to now, the transverse correlation scale of measurement matrices are usually constant. This paper explores a new possibility of constructing highly efficient measurement matrices with multi-correlation scales. Comparisons between the simulational and experimental results show that the multi-correlation-scale measurement matrices are highly efficient and accurate in sampling and image reconstruction and have a better antinoise ability than the existing constant-correlation-scale measurement matrices.

© 2014 Optical Society of America

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  1. P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
    [CrossRef]
  2. B. Sun, S. S. Welsh, M. P. Edgar, J. H. Shapiro, and M. J. Padgett, “Normalized ghost imaging,” Opt. Express 20, 16892–16901 (2012).
    [CrossRef]
  3. W. Gong and S. Han, “A method to improve the visibility of ghost images obtained by thermal light,” Phys. Lett. A 374, 1005–1008 (2010).
    [CrossRef]
  4. F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010).
    [CrossRef]
  5. D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
    [CrossRef]
  6. E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
  7. E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207–1223 (2006).
    [CrossRef]
  8. E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
    [CrossRef]
  9. O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95, 131110 (2009).
    [CrossRef]
  10. W. Gong and S. Han, “Super-resolution ghost imaging via compressive sampling reconstruction,” arXiv:0910.4823 (2009).
  11. J. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 061802 (2008).
    [CrossRef]
  12. 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]
  13. M. Lu, X. Shen, and S. Han, “Ghost imaging via compressive sampling based on digital micromirror device,” Acta Opt. Sin. 31, 0711002 (2011).
    [CrossRef]
  14. R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012).
    [CrossRef]
  15. C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
    [CrossRef]
  16. M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
    [CrossRef]
  17. S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008).
    [CrossRef]
  18. D. Donoho and V. Stodden, “Breakdown point of model selection when the number of variables exceeds the number of observations,” in International Joint Conference on Neural Networks, 2006 (IEEE, 2006), pp. 1916–1921.

2013 (1)

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

2012 (4)

R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012).
[CrossRef]

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

B. Sun, S. S. Welsh, M. P. Edgar, J. H. Shapiro, and M. J. Padgett, “Normalized ghost imaging,” Opt. Express 20, 16892–16901 (2012).
[CrossRef]

2011 (1)

M. Lu, X. Shen, and S. Han, “Ghost imaging via compressive sampling based on digital micromirror device,” Acta Opt. Sin. 31, 0711002 (2011).
[CrossRef]

2010 (2)

W. Gong and S. Han, “A method to improve the visibility of ghost images obtained by thermal light,” Phys. Lett. A 374, 1005–1008 (2010).
[CrossRef]

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010).
[CrossRef]

2009 (1)

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

2008 (4)

J. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 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]

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
[CrossRef]

S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008).
[CrossRef]

2006 (3)

D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
[CrossRef]

E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207–1223 (2006).
[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]

Bo, Z.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

Boyd, R. W.

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

Bromberg, Y.

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

Candes, E.

E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).

Candes, E. J.

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
[CrossRef]

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207–1223 (2006).
[CrossRef]

Chan, K. W. C.

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

Chen, M.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[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]

Deacon, K.

R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012).
[CrossRef]

Donoho, D.

D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
[CrossRef]

D. Donoho and V. Stodden, “Breakdown point of model selection when the number of variables exceeds the number of observations,” in International Joint Conference on Neural Networks, 2006 (IEEE, 2006), pp. 1916–1921.

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.

Ferri, F.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010).
[CrossRef]

Gatti, A.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010).
[CrossRef]

Gong, W.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

W. Gong and S. Han, “A method to improve the visibility of ghost images obtained by thermal light,” Phys. Lett. A 374, 1005–1008 (2010).
[CrossRef]

W. Gong and S. Han, “Super-resolution ghost imaging via compressive sampling reconstruction,” arXiv:0910.4823 (2009).

Han, S.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

M. Lu, X. Shen, and S. Han, “Ghost imaging via compressive sampling based on digital micromirror device,” Acta Opt. Sin. 31, 0711002 (2011).
[CrossRef]

W. Gong and S. Han, “A method to improve the visibility of ghost images obtained by thermal light,” Phys. Lett. A 374, 1005–1008 (2010).
[CrossRef]

S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008).
[CrossRef]

W. Gong and S. Han, “Super-resolution ghost imaging via compressive sampling reconstruction,” arXiv:0910.4823 (2009).

Katz, O.

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

Krogstad, M.

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

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, E.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

Lu, M.

M. Lu, X. Shen, and S. Han, “Ghost imaging via compressive sampling based on digital micromirror device,” Acta Opt. Sin. 31, 0711002 (2011).
[CrossRef]

Lugiato, L. A.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010).
[CrossRef]

Magatti, D.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010).
[CrossRef]

Meyers, R.

R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012).
[CrossRef]

O’Sullivan, M. N.

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

Padgett, M. J.

Romberg, J.

E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).

Romberg, J. K.

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207–1223 (2006).
[CrossRef]

Shapiro, J.

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

Shapiro, J. H.

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

B. Sun, S. S. Welsh, M. P. Edgar, J. H. Shapiro, and M. J. Padgett, “Normalized ghost imaging,” Opt. Express 20, 16892–16901 (2012).
[CrossRef]

Shen, X.

M. Lu, X. Shen, and S. Han, “Ghost imaging via compressive sampling based on digital micromirror device,” Acta Opt. Sin. 31, 0711002 (2011).
[CrossRef]

Shi, Z.

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

Shih, Y.

R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012).
[CrossRef]

Silberberg, Y.

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

Stodden, V.

D. Donoho and V. Stodden, “Breakdown point of model selection when the number of variables exceeds the number of observations,” in International Joint Conference on Neural Networks, 2006 (IEEE, 2006), pp. 1916–1921.

Sun, B.

Sun, T.

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]

Takhar, D.

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]

Tao, Q.

S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008).
[CrossRef]

Tao, T.

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207–1223 (2006).
[CrossRef]

E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).

Tunick, A.

R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012).
[CrossRef]

Wakin, M. B.

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
[CrossRef]

Wang, H.

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

Welsh, S. S.

Xia, S.

S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008).
[CrossRef]

Xu, W.

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

Xu, X.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

Yan-Feng, B.

S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008).
[CrossRef]

Zerom, P.

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

Zhao, C.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

Acta Opt. Sin. (1)

M. Lu, X. Shen, and S. Han, “Ghost imaging via compressive sampling based on digital micromirror device,” Acta Opt. Sin. 31, 0711002 (2011).
[CrossRef]

Appl. Phys. Lett. (3)

R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012).
[CrossRef]

C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012).
[CrossRef]

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

Chin. Phys. Lett. (1)

S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008).
[CrossRef]

Commun. Pure Appl. Math. (1)

E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207–1223 (2006).
[CrossRef]

IEEE Signal Process. Mag. (2)

E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (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]

IEEE Trans. Inf. Theory (2)

D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
[CrossRef]

E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).

Opt. Express (1)

Opt. Photon. J. (1)

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013).
[CrossRef]

Phys. Lett. A (1)

W. Gong and S. Han, “A method to improve the visibility of ghost images obtained by thermal light,” Phys. Lett. A 374, 1005–1008 (2010).
[CrossRef]

Phys. Rev. A (2)

P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012).
[CrossRef]

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

Phys. Rev. Lett. (1)

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010).
[CrossRef]

Other (2)

W. Gong and S. Han, “Super-resolution ghost imaging via compressive sampling reconstruction,” arXiv:0910.4823 (2009).

D. Donoho and V. Stodden, “Breakdown point of model selection when the number of variables exceeds the number of observations,” in International Joint Conference on Neural Networks, 2006 (IEEE, 2006), pp. 1916–1921.

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Figures (5)

Fig. 1.
Fig. 1.

Experimental setup.

Fig. 2.
Fig. 2.

Simulation and experimental results with sampling rate β=0.6. (a1)–(a6) The detection matrices with δi of 32, 16, 8, 4, 2, and 1 pixel, (b8) the continuous target Lena, (c8) the sparse target butterfly, (d8) the sparse target resolution plane, (b1)–(b6) the simulation results of Lena reconstructed by the corresponding measurement matrices, (c1)–(c6) the simulation results of the butterfly reconstructed by the corresponding measurement matrices, (d1)–(d6) the experimental results of the resolution plane reconstructed by the corresponding measurement matrices, (b7), (c7), (d7) the reconstruction results using multi-correlation-scale measurement matrices.

Fig. 3.
Fig. 3.

Simulation and experimental results. (a1) The continuous target Lena, (d1) the sparse target butterfly, (g1) the sparse target resolution plane; (2)–(6) the sampling rate β=M/N is 0.2, 0.4, 0.6, 0.8, and 1.0, respectively; (a)–(f) the simulation results; (g)–(i) the experimental results; (a2)–(a6), (d2)–(d6), (g2)–(g6) the results reconstructed by the constant-correlation-scale measurement matrices with δ of 1 pixel; (b2)–(b6), (e2)–(e6), (h2)–(h6) the results reconstructed by the multi-correlation-scale measurement matrices in the method multi-2; (c2)–(c6), (f2)–(f6), (i2)–(i6) the results reconstructed by the multi-correlation-scale measurement matrices in the method multi-1.

Fig. 4.
Fig. 4.

RMSE curves with varying sampling rate β. (a) The simulation reconstruction RMSE curves of the butterfly using different measurement matrices, (b) the simulation reconstruction RMSE curves of Lena using different measurement matrices, (c) the experimental reconstruction RMSE curves of the resolution plane using different measurement matrices. The red curves are the targets’ RMSE curves reconstructed by the constant-correlation-scale measurement matrices with δ of 1 pixel, the pink curves are the targets’ RMSE curves reconstructed by the multi-correlation-scale measurement matrices in the method multi-2, and the blue curves are the targets’ RMSE curves reconstructed by the multi-correlation-scale measurement matrices in the method multi-1.

Fig. 5.
Fig. 5.

(a) σ(Y) curves with varying sampling rate β, (b) the RMSE curves with varying Noise/σ(Y). The red curves are the curves reconstructed by the constant-correlation-scale measurement matrices with δ of 1 pixel, the pink curves are the curves reconstructed by the multi-correlation-scale measurement matrices in multi-2, and the blue curves are the curves reconstructed by the multi-correlation-scale measurement matrices in multi-1.

Tables (1)

Tables Icon

Table 1. Comparison Table of the Key Parameters for Constructing the Measurement Matrix Using the Methods of Multi-1 and Multi-2a

Equations (1)

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minYΦX22+τX1,

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