Abstract

Imaging and edge detection have been widely applied and played an important role in security checking and medical diagnosis. However, as we know, most edge detection based on ghost imaging system requires large measurement times and the target object image cannot be provided directly. In this work, a new edge detection based on joint iteration of projected Landweber iteration regularization and guided filter ghost imaging method has been proposed, which can improve the feature detection quality in ghost imaging. This method can also achieve high-quality imaging. Simulation and experiment results show that the spatial information and edge information of target object are successfully recovered from the random speckle patterns without special coding under a low measurement times, and the edge image quality is improved remarkably. This approach improves the the applicability of ghost imaging and can satisfy the practical application fields of imaging and edge detection at the same time.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (2)

H.-D. Ren, L. Wang, and S.-M. Zhao, “Efficient edge detection based on ghost imaging,” OSA Continuum 2(1), 64–73 (2019).
[Crossref]

A. M. Kingston, G. R. Myers, D. Pelliccia, I. D. Svalbe, and D. M. Paganin, “X-ray ghost-tomography: Artefacts, dose distribution, and mask considerations,” IEEE Trans. Comput. Imaging 5(1), 136–149 (2019).
[Crossref]

2018 (9)

A. M. Kingston, G. R. Myers, M. P. Olbinado, A. Rack, D. Pelliccia, and D. M. Paganin, “Practical x-ray ghost imaging,” Microsc. Microanal. 24(S2), 134–135 (2018).
[Crossref]

L. Wang, L. Zou, and S. Zhao, “Edge detection based on subpixel-speckle-shifting ghost imaging,” Opt. Commun. 407, 181–185 (2018).
[Crossref]

S. Yuan, D. Xiang, X. Liu, X. Zhou, and P. Bing, “Edge detection based on computational ghost imaging with structured illuminations,” Opt. Commun. 410, 350–355 (2018).
[Crossref]

H. Ren, S. Zhao, and J. Gruska, “Edge detection based on single-pixel imaging,” Opt. Express 26(5), 5501–5511 (2018).
[Crossref]

H. Huang, C. Zhou, T. Tian, D. Liu, and L. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412, 60–65 (2018).
[Crossref]

A. M. Kingston, D. Pelliccia, A. Rack, M. P. Olbinado, Y. Cheng, G. R. Myers, and D. M. Paganin, “Ghost tomography,” Optica 5(12), 1516–1520 (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]

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. Li, F. Cropp, K. Kabra, T. Lane, G. Wetzstein, P. Musumeci, and D. Ratner, “Electron ghost imaging,” Phys. Rev. Lett. 121(11), 114801 (2018).
[Crossref]

2017 (1)

2016 (5)

W. Gong, H. Yu, C. Zhao, Z. Bo, M. Chen, and W. Xu, “Improving the imaging quality of ghost imaging lidar via sparsity constraint by time-resolved technique,” Remote Sens. 8(12), 991 (2016).
[Crossref]

H. Yu, R. Lu, S. Han, H. Xie, G. Du, T. Xiao, and D. Zhu, “Fourier-transform ghost imaging with hard x rays,” Phys. Rev. Lett. 117(11), 113901 (2016).
[Crossref]

D. Pelliccia, A. Rack, M. Scheel, V. Cantelli, and D. M. Paganin, “Experimental x-ray ghost imaging,” Phys. Rev. Lett. 117(11), 113902 (2016).
[Crossref]

W. Gong, C. Zhao, H. Yu, M. Chen, W. Xu, and S. Han, “Three-dimensional ghost imaging lidar via sparsity constraint,” Sci. Rep. 6(1), 26133 (2016).
[Crossref]

T. Mao, Q. Chen, W. He, Y. Zou, H. Dai, and G. Gu, “Speckle-shifting ghost imaging,” IEEE Photonics J. 8(4), 1–10 (2016).
[Crossref]

2015 (6)

W. Gong and S. Han, “High-resolution far-field ghost imaging via sparsity constraint,” Sci. Rep. 5(1), 9280 (2015).
[Crossref]

W. Gong, “High-resolution pseudo-inverse ghost imaging,” Photonics Res. 3(5), 234–237 (2015).
[Crossref]

M.-J. Sun, M.-F. Li, and L.-A. Wu, “Nonlocal imaging of a reflective object using positive and negative correlations,” Appl. Opt. 54(25), 7494–7499 (2015).
[Crossref]

H. Yu, E. Li, W. Gong, and S. Han, “Structured image reconstruction for three-dimensional ghost imaging lidar,” Opt. Express 23(11), 14541–14551 (2015).
[Crossref]

X.-F. Liu, X.-R. Yao, R.-M. Lan, C. Wang, and G.-J. Zhai, “Edge detection based on gradient ghost imaging,” Opt. Express 23(26), 33802–33811 (2015).
[Crossref]

M. Kmieć and A. Glowacz, “Object detection in security applications using dominant edge directions,” Pattern Recognit. Lett. 52, 72–79 (2015).
[Crossref]

2014 (2)

2013 (1)

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013).
[Crossref]

2012 (3)

2010 (2)

F. Ferri, D. Magatti, L. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

X. Li, S. Zhang, X. Pan, P. Dale, and R. Cropp, “Straight road edge detection from high-resolution remote sensing images based on the ridgelet transform with the revised parallel-beam radon transform,” Int. J. Remote. Sens. 31(19), 5041–5059 (2010).
[Crossref]

2009 (3)

K. W. C. Chan, M. N. O’Sullivan, and R. W. Boyd, “High-order thermal ghost imaging,” Opt. Lett. 34(21), 3343–3345 (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]

A. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008).
[Crossref]

2004 (1)

A. Gatti, E. Brambilla, M. Bache, and L. A. Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

2001 (1)

Q. Jin and U. Amato, “A discrete scheme of landweber iteration for solving nonlinear ill-posed problems,” J. Math. Analysis Appl. 253(1), 187–203 (2001).
[Crossref]

1997 (1)

M. Piana and M. Bertero, “Projected landweber method and preconditioning,” Inverse Probl. 13(2), 441–463 (1997).
[Crossref]

1995 (1)

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

1986 (1)

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986).
[Crossref]

1979 (1)

I. E. Abdou and W. K. Pratt, “Quantitative design and evaluation of enhancement/thresholding edge detectors,” Proc. IEEE 67(5), 753–763 (1979).
[Crossref]

Abdou, I. E.

I. E. Abdou and W. K. Pratt, “Quantitative design and evaluation of enhancement/thresholding edge detectors,” Proc. IEEE 67(5), 753–763 (1979).
[Crossref]

Amato, U.

Q. Jin and U. Amato, “A discrete scheme of landweber iteration for solving nonlinear ill-posed problems,” J. Math. Analysis Appl. 253(1), 187–203 (2001).
[Crossref]

Amiot, C.

C. Amiot, P. Ryczkowski, A. T. Friberg, J. M. Dudley, and G. Genty, “Ghost optical coherence tomography,” arXiv preprint arXiv:1810.03380 (2018).

Bache, M.

A. Gatti, E. Brambilla, M. Bache, and L. A. Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

Bertero, M.

M. Piana and M. Bertero, “Projected landweber method and preconditioning,” Inverse Probl. 13(2), 441–463 (1997).
[Crossref]

Bing, P.

S. Yuan, D. Xiang, X. Liu, X. Zhou, and P. Bing, “Edge detection based on computational ghost imaging with structured illuminations,” Opt. Commun. 410, 350–355 (2018).
[Crossref]

Bo, Z.

W. Gong, H. Yu, C. Zhao, Z. Bo, M. Chen, and W. Xu, “Improving the imaging quality of ghost imaging lidar via sparsity constraint by time-resolved technique,” Remote Sens. 8(12), 991 (2016).
[Crossref]

Boyd, R. W.

Brambilla, E.

A. Gatti, E. Brambilla, M. Bache, and L. A. Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

Bromberg, Y.

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]

Canny, J.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(6), 679–698 (1986).
[Crossref]

Cantelli, V.

D. Pelliccia, A. Rack, M. Scheel, V. Cantelli, and D. M. Paganin, “Experimental x-ray ghost imaging,” Phys. Rev. Lett. 117(11), 113902 (2016).
[Crossref]

Cao, J.

Chan, K. W. C.

Chen, L.-M.

Chen, M.

W. Gong, H. Yu, C. Zhao, Z. Bo, M. Chen, and W. Xu, “Improving the imaging quality of ghost imaging lidar via sparsity constraint by time-resolved technique,” Remote Sens. 8(12), 991 (2016).
[Crossref]

W. Gong, C. Zhao, H. Yu, M. Chen, W. Xu, and S. Han, “Three-dimensional ghost imaging lidar via sparsity constraint,” Sci. Rep. 6(1), 26133 (2016).
[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(14), 141123 (2012).
[Crossref]

Chen, Q.

T. Mao, Q. Chen, W. He, Y. Zou, H. Dai, and G. Gu, “Speckle-shifting ghost imaging,” IEEE Photonics J. 8(4), 1–10 (2016).
[Crossref]

Chen, X.-H.

Cheng, Y.

Cropp, F.

S. Li, F. Cropp, K. Kabra, T. Lane, G. Wetzstein, P. Musumeci, and D. Ratner, “Electron ghost imaging,” Phys. Rev. Lett. 121(11), 114801 (2018).
[Crossref]

Cropp, R.

X. Li, S. Zhang, X. Pan, P. Dale, and R. Cropp, “Straight road edge detection from high-resolution remote sensing images based on the ridgelet transform with the revised parallel-beam radon transform,” Int. J. Remote. Sens. 31(19), 5041–5059 (2010).
[Crossref]

Dai, H.

T. Mao, Q. Chen, W. He, Y. Zou, H. Dai, and G. Gu, “Speckle-shifting ghost imaging,” IEEE Photonics J. 8(4), 1–10 (2016).
[Crossref]

Dale, P.

X. Li, S. Zhang, X. Pan, P. Dale, and R. Cropp, “Straight road edge detection from high-resolution remote sensing images based on the ridgelet transform with the revised parallel-beam radon transform,” Int. J. Remote. Sens. 31(19), 5041–5059 (2010).
[Crossref]

Du, G.

H. Yu, R. Lu, S. Han, H. Xie, G. Du, T. Xiao, and D. Zhu, “Fourier-transform ghost imaging with hard x rays,” Phys. Rev. Lett. 117(11), 113901 (2016).
[Crossref]

Dudley, J. M.

C. Amiot, P. Ryczkowski, A. T. Friberg, J. M. Dudley, and G. Genty, “Ghost optical coherence tomography,” arXiv preprint arXiv:1810.03380 (2018).

Edgar, M. P.

Ferri, F.

F. Ferri, D. Magatti, L. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

Friberg, A. T.

C. Amiot, P. Ryczkowski, A. T. Friberg, J. M. Dudley, and G. Genty, “Ghost optical coherence tomography,” arXiv preprint arXiv:1810.03380 (2018).

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, F.

Gatti, A.

F. Ferri, D. Magatti, L. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

A. Gatti, E. Brambilla, M. Bache, and L. A. Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

Genty, G.

C. Amiot, P. Ryczkowski, A. T. Friberg, J. M. Dudley, and G. Genty, “Ghost optical coherence tomography,” arXiv preprint arXiv:1810.03380 (2018).

Glowacz, A.

M. Kmieć and A. Glowacz, “Object detection in security applications using dominant edge directions,” Pattern Recognit. Lett. 52, 72–79 (2015).
[Crossref]

Gong, W.

W. Gong, C. Zhao, H. Yu, M. Chen, W. Xu, and S. Han, “Three-dimensional ghost imaging lidar via sparsity constraint,” Sci. Rep. 6(1), 26133 (2016).
[Crossref]

W. Gong, H. Yu, C. Zhao, Z. Bo, M. Chen, and W. Xu, “Improving the imaging quality of ghost imaging lidar via sparsity constraint by time-resolved technique,” Remote Sens. 8(12), 991 (2016).
[Crossref]

W. Gong, “High-resolution pseudo-inverse ghost imaging,” Photonics Res. 3(5), 234–237 (2015).
[Crossref]

W. Gong and S. Han, “High-resolution far-field ghost imaging via sparsity constraint,” Sci. Rep. 5(1), 9280 (2015).
[Crossref]

H. Yu, E. Li, W. Gong, and S. Han, “Structured image reconstruction for three-dimensional ghost imaging lidar,” Opt. Express 23(11), 14541–14551 (2015).
[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(14), 141123 (2012).
[Crossref]

Gruska, J.

Gu, G.

T. Mao, Q. Chen, W. He, Y. Zou, H. Dai, and G. Gu, “Speckle-shifting ghost imaging,” IEEE Photonics J. 8(4), 1–10 (2016).
[Crossref]

Guan, J.

Guo, H.

H. Guo, L. Wang, and S. Zhao, “Compressed ghost edge imaging,” arXiv preprint arXiv:1902.09344 (2019).

Guo, S.

Han, S.

W. Gong, C. Zhao, H. Yu, M. Chen, W. Xu, and S. Han, “Three-dimensional ghost imaging lidar via sparsity constraint,” Sci. Rep. 6(1), 26133 (2016).
[Crossref]

H. Yu, R. Lu, S. Han, H. Xie, G. Du, T. Xiao, and D. Zhu, “Fourier-transform ghost imaging with hard x rays,” Phys. Rev. Lett. 117(11), 113901 (2016).
[Crossref]

W. Gong and S. Han, “High-resolution far-field ghost imaging via sparsity constraint,” Sci. Rep. 5(1), 9280 (2015).
[Crossref]

H. Yu, E. Li, W. Gong, and S. Han, “Structured image reconstruction for three-dimensional ghost imaging lidar,” Opt. Express 23(11), 14541–14551 (2015).
[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(14), 141123 (2012).
[Crossref]

Hashimoto, K.

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Appl. Opt. (1)

Appl. Phys. Lett. (2)

O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95(13), 131110 (2009).
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IEEE Photonics J. (1)

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IEEE Trans. Comput. Imaging (1)

A. M. Kingston, G. R. Myers, D. Pelliccia, I. D. Svalbe, and D. M. Paganin, “X-ray ghost-tomography: Artefacts, dose distribution, and mask considerations,” IEEE Trans. Comput. Imaging 5(1), 136–149 (2019).
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IEEE Trans. Pattern Anal. Mach. Intell. (3)

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Figures (8)

Fig. 1.
Fig. 1. Schematic diagram of edge detection based on joint iteration ghost imaging.
Fig. 2.
Fig. 2. The numerical simulation results of the aircraft object, where SNRs and PSNRs are presented together.
Fig. 3.
Fig. 3. The numerical simulation results of the simple gray scale object, where SNRs and PSNRs are presented together.
Fig. 4.
Fig. 4. The numerical simulation results of the complex gray scale object, where SNRs and PSNRs are presented together.
Fig. 5.
Fig. 5. The numerical simulation results for compressive GI (OMP) with guided filter, where SNRs and PSNRs are presented together.
Fig. 6.
Fig. 6. The SNR performance of edge information against DSNR of JIGI method.
Fig. 7.
Fig. 7. The experiment system diagram of computational ghost imaging.
Fig. 8.
Fig. 8. Reconstructed images obtained at different measurement times. (a), (c) and (e) are the experimental imaging results of GI, OMPGF and JIGI respectively, (b) (d) and (f) are the edge detection experimental results of GGI, OMPGF and JIGI respectively.

Equations (24)

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A = [ S 1 ( 1 , 1 ) S 1 ( 1 , 2 ) S 1 ( r , c ) S 2 ( 1 , 1 ) S 2 ( 1 , 2 ) S 2 ( r , c ) S M ( 1 , 1 ) S M ( 1 , 2 ) S M ( r , c ) ] ,
y = [ B ( 1 ) , B ( 2 ) , , B ( M ) ] T ,
[ B ( 1 ) B ( 2 ) B ( M ) ] = [ S ( 1 ) ( 1 , 1 ) S ( 1 ) ( 1 , 2 ) S ( 1 ) ( r , c ) S ( 2 ) ( 1 , 1 ) S ( 2 ) ( 1 , 2 ) S ( 2 ) ( r , c ) S ( M ) ( 1 , 1 ) S ( M ) ( 1 , 2 ) S ( M ) ( r , c ) ] [ T ( 1 , 1 ) T ( 1 , 2 ) T ( r , c ) ] .
G ( 2 ) ( i , j ) = B ( m ) S ( m ) ( i , j ) ,
G ( 2 ) ( i , j ) = 1 M A T y .
x t = x t 1 + α P A T ( y A x t 1 ) ,             t = 1 , 2 , 3 , ,
x 1 = α P A T y ,
q t = guidefilter ( I t , x t ) ,             t = 1 , 2 , 3 , ,
q t i = j W i , j ( I t ) x t j ,
W i , j ( I ) = 1 | ω | 2 k : ( i , j ) ω k [ 1 + ( x i μ k ) ( x j μ k ) σ k 2 + ϵ ] ,
q t i = a k I t i + b k , i ω k ,
q = a I .
E ( a k , b k ) = i ω k ( ( a k I t i + b k x t i ) 2 + ϵ a k 2 ) ,
a k = 1 | ω | i ω k I t i x t i μ k x ¯ t k σ k 2 + ϵ ,
b k = x t ¯ k a k μ k .
q t i = 1 | ω | k : i ω k ( a k I t i + b k ) ,
= a ¯ i I t i + b ¯ i ,
[ q t , a k ] = guidefilter ( I t , x t ) ,             t = 1 , 2 , 3 , .
a k = 1 | ω | i ω k I t i 2 μ k 2 σ k 2 + ϵ ,
σ k 2 = 1 | ω | i ω k I t i 2 μ k 2 .
a k = σ k 2 σ k 2 + ϵ .
SNR = m e a n ( q e d g e ) m e a n ( q b a c k ) ( v a r ( q b a c k ) ) 0.5 ,
PSNR = 10 × l o g 10 [ max V a l 2 M S E ] ,
MSE = 1 r × c i = 1 r j = 1 c [ O e d g e a k ] 2 ,

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