Abstract

Imaging through scattering media is a common practice in many applications of biomedical imaging. Object image would deteriorate into unrecognizable speckle pattern when scattering media is presented. Many methods have been investigated to reconstruct the object image when only speckle pattern is available. In this paper, we demonstrate a method of single-shot imaging through scattering media. This method is based on classification and support vector regression of the measured speckle pattern. We prove the possibility of speckle pattern classification and related formulas are presented. The specified and limited imaging capability without speckle pattern classification is demonstrated. Our proposed approach, that is, speckle pattern classification based support vector regression method, makes up the deficiency. Experimental results show that, with our approach, speckle patterns could be utilized for classification when object images are unavailable, and object images can be reconstructed with high fidelity. The proposed approach for imaging through scattering media is expected to be applicable to various sensing schemes.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. Y. Wang, N. Cao, Z. Liu, and Y. Zhang, “Real-time dynamic MRI using parallel dictionary learning and dynamic total variation,” Neurocomputing 238, 410–419 (2017).
    [Crossref]
  2. R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9, 563 (2015).
    [Crossref] [PubMed]
  3. M. Kim, W. Choi, Y. Choi, C. Yoon, and W. Choi, “Transmission matrix of a scattering medium and its applications in biophotonics,” Opt. Express 23, 12648–12668 (2015).
    [Crossref] [PubMed]
  4. J. Jang, J. Lim, H. Yu, H. Choi, J. Ha, J. H. Park, W. Y. Oh, W. Jang, S. Lee, and Y. Park, “Complex wavefront shaping for optimal depth-selective focusing in optical coherence tomography,” Opt. Express 21, 2890–2902 (2013).
    [Crossref] [PubMed]
  5. B. R. Anderson, P. Price, R. Gunawidjaja, and H. Eilers, “Microgenetic optimization algorithm for optimal wavefront shaping,” Appl. Opt. 54, 1485–1491 (2015).
    [Crossref] [PubMed]
  6. L. Fang, X. Zhang, H. Zuo, and L. Pang, “Focusing light through random scattering media by four-element division algorithm,” Opt. Commun. 407, 301–310 (2018).
    [Crossref]
  7. L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
    [Crossref]
  8. I. M. Vellekoop and A. Mosk, “Focusing coherent light through opaque strongly scattering media,” Opt. Lett. 32, 2309–2311 (2007).
    [Crossref] [PubMed]
  9. A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283 (2012).
    [Crossref]
  10. I. M. Vellekoop, A. Lagendijk, and A. P. Mosk, “Exploiting disorder for perfect focusing,” Nat. Photonics 4, 320 (2010).
    [Crossref]
  11. H. He, Y. Guan, and J. Zhou, “Image restoration through thin turbid layers by correlation with a known object,” Opt. Express 21, 12539–12545 (2013).
    [Crossref] [PubMed]
  12. O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784 (2014).
    [Crossref]
  13. E. Edrei and G. Scarcelli, “Optical imaging through dynamic turbid media using the Fourier-domain shower-curtain effect,” Optica 3, 71–74 (2016).
    [Crossref] [PubMed]
  14. P. Wu, Z. Liang, X. Zhao, L. Su, and L. Song, “Lensless wide-field single-shot imaging through turbid media based on object-modulated speckles,” Appl. Opt. 56, 3335–3341 (2017).
    [Crossref] [PubMed]
  15. Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
    [Crossref]
  16. X.-H. Chen, Q. Liu, K.-H. Luo, and L.-A. Wu, “Lensless ghost imaging with true thermal light,” Opt. Lett. 34, 695–697 (2009).
    [Crossref] [PubMed]
  17. R. E. Meyers, K. S. Deacon, and Y. Shih, “Turbulence-free ghost imaging,” Appl. Phys. Lett. 98, 041801 (2011).
    [Crossref]
  18. Y. Xu, W. Liu, E. Zhang, Q. Li, H. Dai, and P. Chen, “Is ghost imaging intrinsically more powerful against scattering?” Opt. Express 23, 32993–33000 (2015).
    [Crossref]
  19. A. Zhang, Y. He, L. Wu, L. Chen, and B. Wang, “Tabletop x-ray ghost imaging with ultra-low radiation,” Optica 5, 374–377 (2018).
    [Crossref]
  20. R. Horisaki, R. Takagi, and J. Tanida, “Learning-based focusing through scattering media,” Appl. Opt. 56, 4358–4362 (2017).
    [Crossref] [PubMed]
  21. R. Horisaki, R. Takagi, and J. Tanida, “Learning-based imaging through scattering media,” Opt. Express 24, 13738–13743 (2016).
    [Crossref] [PubMed]
  22. H. Chen, Y. Gao, and X. Liu, “Speckle reconstruction method based on machine learning,” Proc. SPIE 10711, 107111U (2018).
  23. Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).
  24. Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).
  25. G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
    [Crossref]
  26. S. H. Wang, P. Phillips, Z. C. Dong, and Y. D. Zhang, “Intelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm,” Neurocomputing 272, 668–676 (2018).
    [Crossref]
  27. M. O. Faruqe and M. A. M. Hasan, “Face recognition using PCA and SVM,” in International Conference on Anti-Counterfeiting, Security, and Identification in Communication (2009), pp. 97–101.
  28. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
    [Crossref]
  29. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
    [Crossref]
  30. H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Kernel dictionary learning,” in IEEE International Conference on Acoustics, Speech and Signal Processing, (2012), pp. 2021–2024.
  31. H. Van Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of non-linear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
    [Crossref] [PubMed]
  32. Z. Wang, Y. Wang, H. Liu, and H. Zhang, “Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition,” IEEE Trans. Image Process. 26, 4578–4590 (2017).
    [Crossref]
  33. Vapnik and N. Vladimir, “The nature of statistical learning theory,” IEEE Trans. Neural Netw. 8, 1564 (1997).
    [Crossref]
  34. Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
    [Crossref]
  35. Y. Kim and P. Nelson, “Optimal regularisation for acoustic source reconstruction by inverse methods,” J. Sound Vib. 275, 463–487 (2004).
    [Crossref]
  36. J. Wahr, M. Molenaar, and F. Bryan, “Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE,” J. Geophys. Res. Solid Earth 103, 30205–30229 (1998).
    [Crossref]
  37. M. Joshi, Chandrashakher, and K. Singh, “Color image encryption and decryption for twin images in fractional Fourier domain,” Opt. Commun. 281, 5713–5720 (2008).
    [Crossref]
  38. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
    [Crossref]
  39. Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” in Asilomar Conference on Signals Systems and Computers (2003), pp. 1398–1402.
  40. H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,” (2017).

2018 (7)

L. Fang, X. Zhang, H. Zuo, and L. Pang, “Focusing light through random scattering media by four-element division algorithm,” Opt. Commun. 407, 301–310 (2018).
[Crossref]

H. Chen, Y. Gao, and X. Liu, “Speckle reconstruction method based on machine learning,” Proc. SPIE 10711, 107111U (2018).

Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).

Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

S. H. Wang, P. Phillips, Z. C. Dong, and Y. D. Zhang, “Intelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm,” Neurocomputing 272, 668–676 (2018).
[Crossref]

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

2017 (4)

R. Horisaki, R. Takagi, and J. Tanida, “Learning-based focusing through scattering media,” Appl. Opt. 56, 4358–4362 (2017).
[Crossref] [PubMed]

Z. Wang, Y. Wang, H. Liu, and H. Zhang, “Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition,” IEEE Trans. Image Process. 26, 4578–4590 (2017).
[Crossref]

Y. Wang, N. Cao, Z. Liu, and Y. Zhang, “Real-time dynamic MRI using parallel dictionary learning and dynamic total variation,” Neurocomputing 238, 410–419 (2017).
[Crossref]

P. Wu, Z. Liang, X. Zhao, L. Su, and L. Song, “Lensless wide-field single-shot imaging through turbid media based on object-modulated speckles,” Appl. Opt. 56, 3335–3341 (2017).
[Crossref] [PubMed]

2016 (3)

2015 (5)

2014 (2)

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784 (2014).
[Crossref]

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

2013 (3)

2012 (1)

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283 (2012).
[Crossref]

2011 (1)

R. E. Meyers, K. S. Deacon, and Y. Shih, “Turbulence-free ghost imaging,” Appl. Phys. Lett. 98, 041801 (2011).
[Crossref]

2010 (1)

I. M. Vellekoop, A. Lagendijk, and A. P. Mosk, “Exploiting disorder for perfect focusing,” Nat. Photonics 4, 320 (2010).
[Crossref]

2009 (2)

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

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[Crossref]

2008 (1)

M. Joshi, Chandrashakher, and K. Singh, “Color image encryption and decryption for twin images in fractional Fourier domain,” Opt. Commun. 281, 5713–5720 (2008).
[Crossref]

2007 (1)

2006 (1)

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[Crossref]

2004 (1)

Y. Kim and P. Nelson, “Optimal regularisation for acoustic source reconstruction by inverse methods,” J. Sound Vib. 275, 463–487 (2004).
[Crossref]

1998 (2)

J. Wahr, M. Molenaar, and F. Bryan, “Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE,” J. Geophys. Res. Solid Earth 103, 30205–30229 (1998).
[Crossref]

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

1997 (1)

Vapnik and N. Vladimir, “The nature of statistical learning theory,” IEEE Trans. Neural Netw. 8, 1564 (1997).
[Crossref]

Aharon, M.

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[Crossref]

Anderson, B. R.

Bengio, Y.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

Bottou, L.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

Bovik, A. C.

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” in Asilomar Conference on Signals Systems and Computers (2003), pp. 1398–1402.

Bruckstein, A.

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[Crossref]

Bryan, F.

J. Wahr, M. Molenaar, and F. Bryan, “Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE,” J. Geophys. Res. Solid Earth 103, 30205–30229 (1998).
[Crossref]

Cai, Y.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Cao, N.

Y. Wang, N. Cao, Z. Liu, and Y. Zhang, “Real-time dynamic MRI using parallel dictionary learning and dynamic total variation,” Neurocomputing 238, 410–419 (2017).
[Crossref]

Cao, Y.

Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).

Chandrashakher,

M. Joshi, Chandrashakher, and K. Singh, “Color image encryption and decryption for twin images in fractional Fourier domain,” Opt. Commun. 281, 5713–5720 (2008).
[Crossref]

Chapman, H. N.

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
[Crossref]

Chellappa, R.

H. Van Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of non-linear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[Crossref] [PubMed]

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Kernel dictionary learning,” in IEEE International Conference on Acoustics, Speech and Signal Processing, (2012), pp. 2021–2024.

Chen, H.

H. Chen, Y. Gao, and X. Liu, “Speckle reconstruction method based on machine learning,” Proc. SPIE 10711, 107111U (2018).

Chen, L.

Chen, P.

Chen, X.-H.

Cheng, W. H.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Cheng, Y.

Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).

Choi, H.

Choi, W.

Choi, Y.

Chu, Z.

Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).

Clinton, N.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Cohen, O.

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
[Crossref]

Dai, H.

Deacon, K. S.

R. E. Meyers, K. S. Deacon, and Y. Shih, “Turbulence-free ghost imaging,” Appl. Phys. Lett. 98, 041801 (2011).
[Crossref]

Dong, Z. C.

S. H. Wang, P. Phillips, Z. C. Dong, and Y. D. Zhang, “Intelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm,” Neurocomputing 272, 668–676 (2018).
[Crossref]

Edrei, E.

Eilers, H.

Elad, M.

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[Crossref]

Eldar, Y. C.

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
[Crossref]

Elsworth, D.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Fang, L.

L. Fang, X. Zhang, H. Zuo, and L. Pang, “Focusing light through random scattering media by four-element division algorithm,” Opt. Commun. 407, 301–310 (2018).
[Crossref]

Faruqe, M. O.

M. O. Faruqe and M. A. M. Hasan, “Face recognition using PCA and SVM,” in International Conference on Anti-Counterfeiting, Security, and Identification in Communication (2009), pp. 97–101.

Fink, M.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784 (2014).
[Crossref]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283 (2012).
[Crossref]

Ganesh, A.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[Crossref]

Gao, Y.

H. Chen, Y. Gao, and X. Liu, “Speckle reconstruction method based on machine learning,” Proc. SPIE 10711, 107111U (2018).

Georgescu, M.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Gigan, S.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784 (2014).
[Crossref]

Guan, Y.

Gunawidjaja, R.

Guo, X.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Ha, J.

Haffner, P.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

Hanson, G.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Hasan, M. A. M.

M. O. Faruqe and M. A. M. Hasan, “Face recognition using PCA and SVM,” in International Conference on Anti-Counterfeiting, Security, and Identification in Communication (2009), pp. 97–101.

He, H.

He, Y.

He, Z.

L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
[Crossref]

Heidmann, P.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784 (2014).
[Crossref]

Horisaki, R.

Horstmeyer, R.

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9, 563 (2015).
[Crossref] [PubMed]

Hu, M.

L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
[Crossref]

Jang, J.

Jang, W.

Joshi, M.

M. Joshi, Chandrashakher, and K. Singh, “Color image encryption and decryption for twin images in fractional Fourier domain,” Opt. Commun. 281, 5713–5720 (2008).
[Crossref]

Katz, O.

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784 (2014).
[Crossref]

Khandelwal, A. K.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Kim, M.

Kim, Y.

Y. Kim and P. Nelson, “Optimal regularisation for acoustic source reconstruction by inverse methods,” J. Sound Vib. 275, 463–487 (2004).
[Crossref]

Lagendijk, A.

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283 (2012).
[Crossref]

I. M. Vellekoop, A. Lagendijk, and A. P. Mosk, “Exploiting disorder for perfect focusing,” Nat. Photonics 4, 320 (2010).
[Crossref]

Lecun, Y.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

Lee, S.

Lerosey, G.

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283 (2012).
[Crossref]

Li, C.

Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).

Li, J.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Li, Q.

Liang, Z.

Lim, J.

Liu, D.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Liu, H.

Z. Wang, Y. Wang, H. Liu, and H. Zhang, “Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition,” IEEE Trans. Image Process. 26, 4578–4590 (2017).
[Crossref]

Liu, Q.

Liu, W.

Liu, X.

H. Chen, Y. Gao, and X. Liu, “Speckle reconstruction method based on machine learning,” Proc. SPIE 10711, 107111U (2018).

Liu, Z.

Y. Wang, N. Cao, Z. Liu, and Y. Zhang, “Real-time dynamic MRI using parallel dictionary learning and dynamic total variation,” Neurocomputing 238, 410–419 (2017).
[Crossref]

Luo, K.-H.

Ma, L.

Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).

Ma, Y.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[Crossref]

Mathews, J. P.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Meyers, R. E.

R. E. Meyers, K. S. Deacon, and Y. Shih, “Turbulence-free ghost imaging,” Appl. Phys. Lett. 98, 041801 (2011).
[Crossref]

Miao, J.

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
[Crossref]

Molenaar, M.

J. Wahr, M. Molenaar, and F. Bryan, “Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE,” J. Geophys. Res. Solid Earth 103, 30205–30229 (1998).
[Crossref]

Mosk, A.

Mosk, A. P.

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283 (2012).
[Crossref]

I. M. Vellekoop, A. Lagendijk, and A. P. Mosk, “Exploiting disorder for perfect focusing,” Nat. Photonics 4, 320 (2010).
[Crossref]

Nasrabadi, N. M.

H. Van Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of non-linear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[Crossref] [PubMed]

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Kernel dictionary learning,” in IEEE International Conference on Acoustics, Speech and Signal Processing, (2012), pp. 2021–2024.

Nelson, P.

Y. Kim and P. Nelson, “Optimal regularisation for acoustic source reconstruction by inverse methods,” J. Sound Vib. 275, 463–487 (2004).
[Crossref]

Nguyen, H. V.

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Kernel dictionary learning,” in IEEE International Conference on Acoustics, Speech and Signal Processing, (2012), pp. 2021–2024.

Oh, W. Y.

Pan, Z.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Pang, L.

L. Fang, X. Zhang, H. Zuo, and L. Pang, “Focusing light through random scattering media by four-element division algorithm,” Opt. Commun. 407, 301–310 (2018).
[Crossref]

Park, J. H.

Park, Y.

Patel, V. M.

H. Van Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of non-linear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[Crossref] [PubMed]

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Kernel dictionary learning,” in IEEE International Conference on Acoustics, Speech and Signal Processing, (2012), pp. 2021–2024.

Phillips, P.

S. H. Wang, P. Phillips, Z. C. Dong, and Y. D. Zhang, “Intelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm,” Neurocomputing 272, 668–676 (2018).
[Crossref]

Price, P.

Qiao, X.

L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
[Crossref]

Ran, G.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Rasul, K.

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,” (2017).

Ruan, H.

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9, 563 (2015).
[Crossref] [PubMed]

Sastry, S. S.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[Crossref]

Scarcelli, G.

Segev, M.

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
[Crossref]

Serrano-Candela, F.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Shechtman, Y.

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
[Crossref]

Shih, Y.

R. E. Meyers, K. S. Deacon, and Y. Shih, “Turbulence-free ghost imaging,” Appl. Phys. Lett. 98, 041801 (2011).
[Crossref]

Simoncelli, E. P.

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” in Asilomar Conference on Signals Systems and Computers (2003), pp. 1398–1402.

Singh, K.

M. Joshi, Chandrashakher, and K. Singh, “Color image encryption and decryption for twin images in fractional Fourier domain,” Opt. Commun. 281, 5713–5720 (2008).
[Crossref]

Song, L.

Stuhlmacher, M. F.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Su, L.

Sun, H.

L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
[Crossref]

Takagi, R.

Tang, L.

Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).

Tanida, J.

Tellman, B.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Van Nguyen, H.

H. Van Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of non-linear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[Crossref] [PubMed]

Vapnik,

Vapnik and N. Vladimir, “The nature of statistical learning theory,” IEEE Trans. Neural Netw. 8, 1564 (1997).
[Crossref]

Vellekoop, I. M.

I. M. Vellekoop, A. Lagendijk, and A. P. Mosk, “Exploiting disorder for perfect focusing,” Nat. Photonics 4, 320 (2010).
[Crossref]

I. M. Vellekoop and A. Mosk, “Focusing coherent light through opaque strongly scattering media,” Opt. Lett. 32, 2309–2311 (2007).
[Crossref] [PubMed]

Vladimir, N.

Vapnik and N. Vladimir, “The nature of statistical learning theory,” IEEE Trans. Neural Netw. 8, 1564 (1997).
[Crossref]

Vollgraf, R.

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,” (2017).

Wahr, J.

J. Wahr, M. Molenaar, and F. Bryan, “Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE,” J. Geophys. Res. Solid Earth 103, 30205–30229 (1998).
[Crossref]

Wang, B.

Wang, C.

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Wang, Q.

Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).

Wang, R. K.

Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).

Wang, S. H.

S. H. Wang, P. Phillips, Z. C. Dong, and Y. D. Zhang, “Intelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm,” Neurocomputing 272, 668–676 (2018).
[Crossref]

Wang, Y.

Z. Wang, Y. Wang, H. Liu, and H. Zhang, “Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition,” IEEE Trans. Image Process. 26, 4578–4590 (2017).
[Crossref]

Y. Wang, N. Cao, Z. Liu, and Y. Zhang, “Real-time dynamic MRI using parallel dictionary learning and dynamic total variation,” Neurocomputing 238, 410–419 (2017).
[Crossref]

Wang, Z.

Z. Wang, Y. Wang, H. Liu, and H. Zhang, “Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition,” IEEE Trans. Image Process. 26, 4578–4590 (2017).
[Crossref]

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” in Asilomar Conference on Signals Systems and Computers (2003), pp. 1398–1402.

Wright, J.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[Crossref]

Wu, L.

Wu, L.-A.

Wu, P.

Xiao, H.

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,” (2017).

Xu, Y.

Yang, A. Y.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[Crossref]

Yang, C.

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9, 563 (2015).
[Crossref] [PubMed]

Yao, Y.

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

Yoon, C.

Yu, H.

Zhang, A.

Zhang, E.

Zhang, H.

Z. Wang, Y. Wang, H. Liu, and H. Zhang, “Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition,” IEEE Trans. Image Process. 26, 4578–4590 (2017).
[Crossref]

Zhang, Q.

Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).

Zhang, X.

L. Fang, X. Zhang, H. Zuo, and L. Pang, “Focusing light through random scattering media by four-element division algorithm,” Opt. Commun. 407, 301–310 (2018).
[Crossref]

Zhang, Y.

Y. Wang, N. Cao, Z. Liu, and Y. Zhang, “Real-time dynamic MRI using parallel dictionary learning and dynamic total variation,” Neurocomputing 238, 410–419 (2017).
[Crossref]

Zhang, Y. D.

S. H. Wang, P. Phillips, Z. C. Dong, and Y. D. Zhang, “Intelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm,” Neurocomputing 272, 668–676 (2018).
[Crossref]

Zhao, X.

Zhou, J.

Zhou, L.

L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
[Crossref]

Zhou, Y.

Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).

Zhuang, B.

L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
[Crossref]

Zuo, H.

L. Fang, X. Zhang, H. Zuo, and L. Pang, “Focusing light through random scattering media by four-element division algorithm,” Opt. Commun. 407, 301–310 (2018).
[Crossref]

Appl. Opt. (3)

Appl. Phys. Lett. (1)

R. E. Meyers, K. S. Deacon, and Y. Shih, “Turbulence-free ghost imaging,” Appl. Phys. Lett. 98, 041801 (2011).
[Crossref]

IEEE Signal Process. Mag. (1)

Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev, “Phase retrieval with application to optical imaging: a contemporary overview,” IEEE Signal Process. Mag. 32, 87–109 (2015).
[Crossref]

IEEE Trans. Image Process. (2)

H. Van Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of non-linear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[Crossref] [PubMed]

Z. Wang, Y. Wang, H. Liu, and H. Zhang, “Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition,” IEEE Trans. Image Process. 26, 4578–4590 (2017).
[Crossref]

IEEE Trans. Neural Netw. (1)

Vapnik and N. Vladimir, “The nature of statistical learning theory,” IEEE Trans. Neural Netw. 8, 1564 (1997).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[Crossref]

IEEE Trans. Signal Process. (1)

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[Crossref]

Int. J. Coal Geol. (1)

Y. Cai, D. Liu, J. P. Mathews, Z. Pan, D. Elsworth, Y. Yao, J. Li, and X. Guo, “Permeability evolution in fractured coal−combining triaxial confinement with X-ray computed tomography, acoustic emission and ultrasonic techniques,” Int. J. Coal Geol. 122, 91–104 (2014).
[Crossref]

J. Geophys. Res. Solid Earth (1)

J. Wahr, M. Molenaar, and F. Bryan, “Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE,” J. Geophys. Res. Solid Earth 103, 30205–30229 (1998).
[Crossref]

J. Sound Vib. (1)

Y. Kim and P. Nelson, “Optimal regularisation for acoustic source reconstruction by inverse methods,” J. Sound Vib. 275, 463–487 (2004).
[Crossref]

Nat. Photonics (4)

O. Katz, P. Heidmann, M. Fink, and S. Gigan, “Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations,” Nat. Photonics 8, 784 (2014).
[Crossref]

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9, 563 (2015).
[Crossref] [PubMed]

A. P. Mosk, A. Lagendijk, G. Lerosey, and M. Fink, “Controlling waves in space and time for imaging and focusing in complex media,” Nat. Photonics 6, 283 (2012).
[Crossref]

I. M. Vellekoop, A. Lagendijk, and A. P. Mosk, “Exploiting disorder for perfect focusing,” Nat. Photonics 4, 320 (2010).
[Crossref]

Neurocomputing (2)

S. H. Wang, P. Phillips, Z. C. Dong, and Y. D. Zhang, “Intelligent Facial Emotion Recognition based on Stationary Wavelet Entropy and Jaya algorithm,” Neurocomputing 272, 668–676 (2018).
[Crossref]

Y. Wang, N. Cao, Z. Liu, and Y. Zhang, “Real-time dynamic MRI using parallel dictionary learning and dynamic total variation,” Neurocomputing 238, 410–419 (2017).
[Crossref]

Opt. Commun. (2)

M. Joshi, Chandrashakher, and K. Singh, “Color image encryption and decryption for twin images in fractional Fourier domain,” Opt. Commun. 281, 5713–5720 (2008).
[Crossref]

L. Fang, X. Zhang, H. Zuo, and L. Pang, “Focusing light through random scattering media by four-element division algorithm,” Opt. Commun. 407, 301–310 (2018).
[Crossref]

Opt. Express (5)

Opt. Int. J. Light Electron. Opt. (1)

L. Zhou, B. Zhuang, H. Sun, Z. He, M. Hu, and X. Qiao, “Speckle phase retrieval and transmission matrix obtaining of turbid media,” Opt. Int. J. Light Electron. Opt. 127, 9911–9916 (2016).
[Crossref]

Opt. Lett. (2)

Optica (2)

Proc. IEEE (1)

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86, 2278–2324 (1998).
[Crossref]

Proc. SPIE (3)

H. Chen, Y. Gao, and X. Liu, “Speckle reconstruction method based on machine learning,” Proc. SPIE 10711, 107111U (2018).

Q. Wang, L. Ma, C. Li, Y. Zhou, and L. Tang, “Fast object recognition method from random measurements of compressive sensing camera,” Proc. SPIE 10679, 1067919 (2018).

Y. Cheng, Y. Cao, Q. Zhang, Z. Chu, and R. K. Wang, “Deep network for retinal disease classification based on limited clinical OCT angiography datasets (Conference Presentation),” Proc. SPIE 10474, 1047407 (2018).

Remote Sens. Environ. (1)

G. Ran, M. F. Stuhlmacher, B. Tellman, N. Clinton, G. Hanson, M. Georgescu, C. Wang, F. Serrano-Candela, A. K. Khandelwal, and W. H. Cheng, “Using landsat and nighttime lights for supervised pixel-based image classification of urban land cover,” Remote Sens. Environ. 205, 253–275 (2018).
[Crossref]

Other (4)

Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” in Asilomar Conference on Signals Systems and Computers (2003), pp. 1398–1402.

H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,” (2017).

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Kernel dictionary learning,” in IEEE International Conference on Acoustics, Speech and Signal Processing, (2012), pp. 2021–2024.

M. O. Faruqe and M. A. M. Hasan, “Face recognition using PCA and SVM,” in International Conference on Anti-Counterfeiting, Security, and Identification in Communication (2009), pp. 97–101.

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Figures (11)

Fig. 1
Fig. 1 Experimental setup of a scattering system. O, objective; P, pinhole; L, lens; SLM, spatial light modulator.
Fig. 2
Fig. 2 Flow chart of the inverse scattering method.
Fig. 3
Fig. 3 Flow chart of speckle pattern classification.
Fig. 4
Fig. 4 Illustrations of different OS pairs from the MNIST OS Dataset. From left to right, each column shows normalized OS pair of a certain class from “0” to “4”. (a) object image of “0”, (b) speckle pattern of (a); (c) object image of “1”, (d) speckle pattern of (c); (e) object image of “2”, (f) speckle pattern of (e); (g) object image of “3”, (h) speckle pattern of (g); (i) object image of “4”, (j) speckle pattern of (i).
Fig. 5
Fig. 5 Illustrations of different OS pairs from the Fashion MNIST OS Dataset. From left to right, each column shows normalized OS pair of a certain class from “T-shirt” to “Sneaker”. (a) object image of “T-shirt”, (b) speckle pattern of (a); (c) object image of “Trouser”, (d) speckle pattern of (c); (e) object image of “Coat”, (f) speckle pattern of (e); (g) object image of “Sandals”, (h) speckle pattern of (g); (i) object image of “Sneaker”, (j) speckle pattern of (i).
Fig. 6
Fig. 6 Reconstructions without classification first before learning the ISF with the MNIST OS Dataset. From left to right, the listed images in each column are, (a) and (b) input object images, (c) and (d) speckle patterns, (e) and (f) reconstructions with ISF learned from digit “O” OS pairs, (g) and (h) reconstructions with ISF learned from digit “4” OS pairs, (i) and (j) reconstructions with ISF learned from OS pairs of all the 10 classes.
Fig. 7
Fig. 7 Reconstructions without classification first before learning the ISF with the Fashion MNIST OS Dataset. From left to right, the listed images in each column are, (a) and (b) input object images, (c) and (d) speckle patterns, (e) and (f) reconstructions with ISF learned from “Trouser” OS pairs, (g) and (h) reconstructions with ISF learned from “Sneaker” OS pairs, (i) and (j) reconstructions with ISF learned from OS pairs of all the 10 classes.
Fig. 8
Fig. 8 Reconstructions using speckle pattern classification based support vector regression with the MNIST OS Database. (a) ∼ (j) show object and reconstruction examples of all the 10 classes from digit “0” to “9”.
Fig. 9
Fig. 9 Reconstructions using speckle pattern classification based support vector regression with the Fashion MNIST OS Database. (a) ∼ (j) show object and reconstruction examples of all the 10 classes from “T-shirt” to “Ankle boots”.
Fig. 10
Fig. 10 Reconstructions based on MNIST Database with different methods. (a) tested original object image; (b) reconstruction with the proposed method; (c) reconstruction with wavefront shaping technique; (d) reconstruction with ghost imaging technique; (e) reconstruction with phase-retrieval based method.
Fig. 11
Fig. 11 Reconstructions based on Fashion MNIST Database with different methods. (a) tested original object image; (b) reconstruction with the proposed method; (c) reconstruction with wavefront shaping technique; (d) reconstruction with ghost imaging technique; (e) reconstruction with phase-retrieval based method.

Tables (3)

Tables Icon

Table 1 The example numbers in the MNIST Database.

Tables Icon

Table 2 Speckle pattern classification accuracies of some related methods on the MNIST OS Database.

Tables Icon

Table 3 Speckle pattern classification accuracies of some related methods on the Fashion MNIST OS Database.

Equations (19)

Equations on this page are rendered with MathJax. Learn more.

E o u t e j ϕ = K E i n ,
( E o u t e j ϕ ) ( E o u t e j ϕ ) * = ( K E i n ) ( K E i n ) * = K E i n ( E i n ) * K * ,
E o u t = f ( E i n ) , E i n = f 1 ( E o u t ) ,
min w , b 1 2 w T w + C n = 1 N max ( 0 , | w T E n o u t + b E n i n | ε ) ,
min β , b 1 2 n = 1 N β n β m exp ( γ | | E n o u t E m o u t | | 2 ) + C n = 1 N max ( 0 , | m = 1 N β m exp ( γ | | E n o u t E m o u t | | 2 ) E n i n | ε ) ,
P S N R ( d B ) = 10 log 10 ( M A X I   2 M S E ) , M S E = 1 p x p y i = 0 p x 1 j = 0 p y 1 [ x ( i , j ) y ( i , j ) ] 2 ,
S S I M ( x , y ) = ( 2 μ x μ y + c 1 ) ( 2 σ x y + c 2 ) ( μ x 2 + μ y 2 + c 1 ) ( σ x 2 + σ y 2 + c 2 ) ,
K = S 1 V 1 D 1 * ,
K K * = ( S 1 V 1 D 1 * ) ( S 1 V 1 D 1 * ) * = S 1 V 1 D 1 * D 1 V 1 * S 1 * = S 1 V 1 V 1 * S 1 * = I .
E i n = S 2 V 2 D 2 * ,
( E o u t e j ϕ ) ( E o u t e j ϕ ) * = K E i n ( E i n ) * K * = ( S 1 V 1 D 1 * ) ( S 2 V 2 D 2 * ) ( S 2 V 2 D 2 * ) * ( S 1 V 1 D 1 * ) * = S 1 V 1 D 1 * S 2 V 2 D 2 * D 2 V 2 * S 2 * D 1 V 1 * S 1 * = S 1 V 1 D 1 * S 2 V 2 V 2 * S 2 * D 1 V 1 * S 1 * = ( S 1 V 1 D 1 * S 2 ) ( V 2 V 2 * ) ( S 1 V 1 D 1 * S 2 ) * .
S 3 ( S 3 ) * = ( S 1 V 1 D 1 * S 2 ) ( S 1 V 1 D 1 * S 2 ) * = S 1 V 1 D 1 * S 2 S 2 * D 1 V 1 * S 1 * = S 1 V 1 D 1 * D 1 V 1 * S 1 * = S 1 V 1 V 1 * S 1 * = I .
( E o u t e j ϕ ) ( E o u t e j ϕ ) * = S 3 V 3 D 3 * ,
E i n ( E i n ) * = ( S 2 V 2 D 2 * ) ( S 2 V 2 D 2 * ) * = S 2 V 2 D 2 * D 2 V 2 * S 2 * = S 2 ( V 2 V 2 * ) S 2 * .
e j ϕ = [ e j ϕ 1 , e j ϕ 2 , , e j ϕ M o u t ] T [ e j ϕ 1         e j ϕ 2                 e j ϕ M o u t ] = φ .
( E o u t e j ϕ ) ( E o u t e j ϕ ) * = ( φ E o u t ) ( φ E o u t ) * = φ E o u t ( E o u t ) * φ * .
E o u t = S 4 V 4 D 4 * ,
E o u t ( E o u t ) * = ( S 4 V 4 D 4 * ) ( S 4 V 4 D 4 * ) * = S 4 V 4 D 4 * D 4 V 4 * S 4 * = S 4 ( V 4 V 4 * ) S 4 * .
( E o u t e j ϕ ) ( E o u t e j ϕ ) * = φ E o u t ( E o u t ) * φ * = φ S 4 V 4 D 4 * ( S 4 V 4 D 4 * ) * φ * = φ S 4 V 4 D 4 * D 4 V 4 * S 4 * φ * = ( φ S 4 ) ( V 4 V 4 * ) ( φ S 4 ) * .

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