Abstract

We demonstrate an imaging technique implementing vertical cavity lasers with extremely low transient times for a greatly simplified realization of a multiexposure laser speckle contrast imaging system. Data from multiexposure laser speckle imaging was observed to more closely agree with absolute velocity measurements using time of flight technique, when compared to long-exposure laser speckle imaging. Furthermore, additional depth information of the vasculature morphology was inferred by accounting for the change in the static scattering from tissue above vessels with respect to the total scattering from blood flow and tissue.

© 2013 Optical Society of America

Full Article  |  PDF Article

References

  • View by:
  • |
  • |
  • |

  1. K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
    [CrossRef]
  2. Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
    [CrossRef]
  3. F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
    [CrossRef]
  4. H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
    [CrossRef]
  5. J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
    [CrossRef]
  6. M. Duarte, M. Davenport, D. Takbar, J. Laska, T. Sun, K. F. Kelly, and R. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25, 83–91 (2008).
    [CrossRef]
  7. R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.
    [CrossRef]
  8. A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
    [CrossRef]
  9. D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
    [CrossRef]
  10. H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.
    [CrossRef]
  11. A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.
    [CrossRef]
  12. L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
    [CrossRef]
  13. G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
    [CrossRef]
  14. G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).
    [CrossRef]
  15. J. Mairal, F. Bach, and J. Ponce, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).
    [CrossRef]
  16. 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).
  17. E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).
  18. E. J. Candés, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
    [CrossRef]

2012

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

2011

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).
[CrossRef]

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

2010

D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
[CrossRef]

J. Mairal, F. Bach, and J. Ponce, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).
[CrossRef]

2008

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

2007

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

2006

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).

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

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

Achim, A.

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[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).

Arce, G.

H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.
[CrossRef]

Arguello, H.

H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.
[CrossRef]

Ashok, A.

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.
[CrossRef]

Bach, F.

J. Mairal, F. Bach, and J. Ponce, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).
[CrossRef]

Baheti, P. K.

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.
[CrossRef]

Baraniuk, R.

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

Bibet, A.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
[CrossRef]

Brady, D. J.

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).

Candés, E. J.

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

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

Chen, J.

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

Chen, X.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Cheng, Y.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Choi, K.

Davenport, M.

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

Duarte, M.

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

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).

Gao, D.

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).
[CrossRef]

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Jacques, L.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
[CrossRef]

John, R.

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Kelly, K. F.

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

Kittle, D.

Laska, J.

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

Leblebici, Y.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
[CrossRef]

Liao, Q.

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

Liu, D.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).
[CrossRef]

Mairal, J.

J. Mairal, F. Bach, and J. Ponce, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).
[CrossRef]

Majidzadeh, V.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
[CrossRef]

Marcia, R. F.

R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.
[CrossRef]

Neifeld, M. A.

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.
[CrossRef]

Niu, R.

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Nunez-Yanez, J.

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

Pan, Q.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Ponce, J.

J. Mairal, F. Bach, and J. Ponce, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).
[CrossRef]

Romberg, J.

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

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

Schmid, A.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
[CrossRef]

Shi, G.

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).
[CrossRef]

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Song, L.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Song, X.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Su, H.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Sun, T.

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

Takbar, D.

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

Tang, L.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Tao, T.

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

Tretter, D.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Vandergheynst, P.

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
[CrossRef]

Wagadarikar, A.

D. Kittle, K. Choi, A. Wagadarikar, and D. J. Brady, “Multiframe image estimation for coded aperture snapshot spectral imagers,” Appl. Opt. 49, 6824–6833 (2010).
[CrossRef]

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Wang, Y.

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Willett, R.

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Willett, R. M.

R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.
[CrossRef]

Wu, K.

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Wu, Y.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Xie, X.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

Yang, J.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Yang, W.

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

Zhang, Q.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Zhao, Y.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Zhou, F.

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

Zhou, J.

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

Appl. Opt.

EURASIP J. Adv. Signal Process.

Y. Zhao, J. Yang, Q. Zhang, L. Song, Y. Cheng, and Q. Pan, “Hyperspectral imagery super-resolution by sparse representation and spectral regularization,” EURASIP J. Adv. Signal Process. 87, 1–10 (2011).
[CrossRef]

Found. Comput. Math.

E. J. Candés and J. Romberg, “Quantitative robust uncertainty principles and optimally sparse decompositions,” Found. Comput. Math. 6, 227–254 (2006).

IEEE Signal Process. Lett.

J. Chen, J. Nunez-Yanez, and A. Achim, “Video super-resolution using generalized Gaussian Markov random fields,” IEEE Signal Process. Lett. 19, 63–66 (2012).
[CrossRef]

IEEE Signal Process. Mag.

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

IEEE Trans. Image Process.

G. Shi, D. Gao, X. Song, X. Xie, X. Chen, and D. Liu, “High-resolution imaging via moving random exposure and its simulation,” IEEE Trans. Image Process. 20, 276–282 (2011).
[CrossRef]

H. Su, L. Tang, Y. Wu, D. Tretter, and J. Zhou, “Spatially adaptive block-based super-resolution,” IEEE Trans. Image Process. 21, 1031–1045 (2012).
[CrossRef]

IEEE Trans. Inf. Theory

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

IEEE Trans. Signal Process.

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).

IEEE. Trans. Image Process.

F. Zhou, W. Yang, and Q. Liao, “A coarse-to-fine subpixel registration method to recover local perspective deformation in the application of image super-resolution,” IEEE. Trans. Image Process. 21, 53–66 (2012).
[CrossRef]

J. Mach. Learn. Res.

J. Mairal, F. Bach, and J. Ponce, “Online learning for matrix factorization and sparse coding,” J. Mach. Learn. Res. 11, 19–60 (2010).
[CrossRef]

Opt. Eng.

K. Wu, R. Niu, and Y. Wang, “Super-resolution land-cover mapping based on the selective endmember spectral mixture model in hyperspectral imagery,” Opt. Eng. 50, 126201 (2011).
[CrossRef]

Proc. SPIE

A. Wagadarikar, R. John, and R. Willett, “Single disperser design for compressive, single-snapshot spectral imaging,” Proc. SPIE 6714, 67140A (2007).
[CrossRef]

Spacecraft Recovery Remote Sensing

G. Shi, D. Liu, and D. Gao, “High-resolution computational spectral imaging of remote sensing based on coded sensing,” Spacecraft Recovery Remote Sensing 32, 60–66 (2011) (in Chinese).
[CrossRef]

Other

R. F. Marcia and R. M. Willett, “Compressive coded aperture superresolution image reconstruction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2008), pp. 833–836.
[CrossRef]

H. Arguello and G. Arce, “Code aperture design for compressive spectral imaging,” in Proceedings of the 18th European Signal Processing Conference (European Association for Signal Processing (EURASIP), 2010), pp. 1434–1438.
[CrossRef]

A. Ashok, P. K. Baheti, and M. A. Neifeld, “Projective imager design with task-specific information,” in Proceedings of Frontiers in Optics (Optical Society of America, 2007), paper FThQ4.
[CrossRef]

L. Jacques, P. Vandergheynst, A. Bibet, V. Majidzadeh, A. Schmid, and Y. Leblebici, “CMOS compressed imaging by random convolution,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1113–1116.
[CrossRef]

Cited By

OSA participates in CrossRef's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (5)

Fig. 1.
Fig. 1.

(a) Experimental setup of the imaging system. (b) Camera and laser onset time schematic; the VCSEL illumination was initiated 300 μs (much greater time than laser transient time) before the camera exposure time onset, and (c) changes of speckle contrast ratio with current on phantom (red dots) and in vivo (red crossed circle) and changes of optical power with current (blue squares). Note that K values do not change more than 15%, from 1.05 to 3.0 mA, while providing 7-fold increase in optical power.

Fig. 2.
Fig. 2.

(a) 300 averaged speckle contrast ratio map from 50 μs to 30 ms obtained by changing exposure time of VCSELs. (b) Speckle contrast ratio data from a vein fitted to both a Gaussian and Lorentzian model. (c) ρ map obtained from VCSELs. (d) Relative velocity map obtained using VCSELs.

Fig. 3.
Fig. 3.

(a) Comparison of long-exposure relative velocity map to absolute velocities obtained using a green LED. ρ values are shown on each point. A linear fit through the origin is in green. (b) Comparison of a Lorentzian multiexposure speckle imaging to absolute velocities. Significant improvement in a linear fit through the origin is found as compared to using the long-exposure approximation. (c) Comparison of a Gaussian multiexposure speckle imaging to absolute velocities. There are no significant differences as compared to using a Lorentzian model.

Fig. 4.
Fig. 4.

(a), (b) Percentage changes of ρ for Lorentzian and Gaussian models from 200 to +600 μm. Changes of ±20% happen in vessels. (c)–(e) Percentage changes of relative velocity maps for long-exposure and Lorentzian/Gaussian models. Velocity measurements vary by as much as ±80% even when multi-exposure fitting is used.

Fig. 5.
Fig. 5.

(a) Changes in ρ along a vessel with no major branching in/out suggests ρ can be used to estimate depth. (b) Comparing best focus using a green LED to (1/ρ1) values. A correlation is observed which allows for estimation of vessel depth. (c) 3D map of in-focus vessels with depth estimated in microns using linear fit of (b). Color represents relative velocity as estimated from multiexposure data.

Equations (6)

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

K=σI.
τc=2TK2.
K(T,τc)=(βρ2e2x1+2x2x2+4βρ(1ρ)ex1+xx2+νne+νnoise)12,
limx0K=β+νnoise=βρ(2ρ)+νne+νnoise.
K(T,τc)=(βρ2e2x21+2πxerf(2x)2x2+2βρ(1ρ)ex21+πxerf(x)x2+β(1ρ)2+νnoise)12.
γIsIf=(1ρ1).

Metrics