Abstract

Microscopy is an essential tool in a huge range of research areas. Until now, microscopy has been largely restricted to imaging in the visible region of the electromagnetic spectrum. Here we present a microscope system that uses single-pixel imaging techniques to produce images simultaneously in the visible and shortwave infrared. We apply our microscope to the inspection of various objects, including a silicon CMOS sensor, highlighting the complementarity of the visible and shortwave infrared wavebands. The system is capable of producing images with resolutions between 32×32 and 128×128 pixels at corresponding frame rates between 10 and 0.6 Hz. We introduce a compressive technique that does not require postprocessing, resulting in a predicted frame rate increase by a factor 8 from a compressive ratio of 12.5% with only 28% relative error.

© 2014 Optical Society of America

Full Article  |  PDF Article
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References

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  1. M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008).
    [Crossref]
  2. J. Mertz, Introduction to Optical Microscopy (Roberts, 2010).
  3. T. Zimmermann, J. Rietdorf, R. Pepperkok, “Spectral imaging and its applications in live cell microscopy,” FEBS Lett. 546, 87–92 (2003).
    [Crossref]
  4. Y. Wu, P. Ye, I. O. Mirza, G. R. Arce, D. W. Prather, “Experimental demonstration of an optical-sectioning compressive sensing microscope (CSM),” Opt. Express 18, 24565–24578 (2010).
    [Crossref]
  5. V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
    [Crossref]
  6. D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006).
    [Crossref]
  7. E. J. Candès, J. Romberg, T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
    [Crossref]
  8. N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
    [Crossref]
  9. E. J. Candès, M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
    [Crossref]
  10. C. J. R. Sheppard, T. Wilson, “Image formation in confocal scanning microscopes,” Optik 55, 331–342 (1980).
  11. W. Pratt, J. Kane, H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57, 58–68 (1969).
    [Crossref]
  12. R. Marcia, R. M. Willett, “Compressive coded aperture video reconstruction,” in Proceedings of European Signal Processing Conference (EUSIPCO) (2008).
  13. J. Zheng, E. L. Jacobs, “Video compressive sensing using spatial domain sparsity,” Opt. Eng. 48, 087006 (2009).
    [Crossref]
  14. Z. Liu, A. Y. Elezzabi, H. V. Zhao, “Maximum frame rate video acquisition using adaptive compressed sensing,” IEEE Trans. Circuits Syst. Video Technol. 21, 1704–1718 (2011).
    [Crossref]
  15. J. E. Fowler, S. Mun, E. W. Tramel, “Block-based compressed sensing of images and video,” Found. Trends Signal Process. 4, 297–416 (2012).
  16. L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.
  17. P. Llull, X. Liao, X. Yuan, J. Yang, D. Kittle, L. Carin, G. Sapiro, D. J. Brady, “Coded aperture compressive temporal imaging,” Opt. Express 21, 10526–10545 (2013).
    [Crossref]
  18. I. Noor, E. L. Jacobs, “Adaptive compressive sensing algorithm for video acquisition using a single-pixel camera,” J. Electron. Imaging 22, 021013 (2013).
    [Crossref]
  19. A. C. Sankaranarayanan, C. Studer, R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in International Conference on Computational Photography (ICCP) (IEEE, 2012).
  20. T. Goldstein, L. Xu, K. F. Kelly, R. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.3405 (2013).

2013 (2)

P. Llull, X. Liao, X. Yuan, J. Yang, D. Kittle, L. Carin, G. Sapiro, D. J. Brady, “Coded aperture compressive temporal imaging,” Opt. Express 21, 10526–10545 (2013).
[Crossref]

I. Noor, E. L. Jacobs, “Adaptive compressive sensing algorithm for video acquisition using a single-pixel camera,” J. Electron. Imaging 22, 021013 (2013).
[Crossref]

2012 (2)

J. E. Fowler, S. Mun, E. W. Tramel, “Block-based compressed sensing of images and video,” Found. Trends Signal Process. 4, 297–416 (2012).

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

2011 (1)

Z. Liu, A. Y. Elezzabi, H. V. Zhao, “Maximum frame rate video acquisition using adaptive compressed sensing,” IEEE Trans. Circuits Syst. Video Technol. 21, 1704–1718 (2011).
[Crossref]

2010 (1)

2009 (1)

J. Zheng, E. L. Jacobs, “Video compressive sensing using spatial domain sparsity,” Opt. Eng. 48, 087006 (2009).
[Crossref]

2008 (2)

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

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

2006 (3)

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

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

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

2003 (1)

T. Zimmermann, J. Rietdorf, R. Pepperkok, “Spectral imaging and its applications in live cell microscopy,” FEBS Lett. 546, 87–92 (2003).
[Crossref]

1980 (1)

C. J. R. Sheppard, T. Wilson, “Image formation in confocal scanning microscopes,” Optik 55, 331–342 (1980).

1969 (1)

W. Pratt, J. Kane, H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57, 58–68 (1969).
[Crossref]

Andrews, H. C.

W. Pratt, J. Kane, H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57, 58–68 (1969).
[Crossref]

Arce, G. R.

Baraniuk, R.

T. Goldstein, L. Xu, K. F. Kelly, R. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.3405 (2013).

Baraniuk, R. G.

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

L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.

A. C. Sankaranarayanan, C. Studer, R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in International Conference on Computational Photography (ICCP) (IEEE, 2012).

Bobin, J.

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

Brady, D.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Brady, D. J.

Candes, E.

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

Candès, E. J.

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

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

Carin, L.

Chahid, M.

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

Dahan, M.

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

Davenport, M. A.

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

Donoho, D. L.

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

Duarte, M. F.

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

Elezzabi, A. Y.

Z. Liu, A. Y. Elezzabi, H. V. Zhao, “Maximum frame rate video acquisition using adaptive compressed sensing,” IEEE Trans. Circuits Syst. Video Technol. 21, 1704–1718 (2011).
[Crossref]

Feldman, M.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Fiddy, M.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Fowler, J. E.

J. E. Fowler, S. Mun, E. W. Tramel, “Block-based compressed sensing of images and video,” Found. Trends Signal Process. 4, 297–416 (2012).

Goldstein, T.

T. Goldstein, L. Xu, K. F. Kelly, R. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.3405 (2013).

Jacobs, E. L.

I. Noor, E. L. Jacobs, “Adaptive compressive sensing algorithm for video acquisition using a single-pixel camera,” J. Electron. Imaging 22, 021013 (2013).
[Crossref]

J. Zheng, E. L. Jacobs, “Video compressive sensing using spatial domain sparsity,” Opt. Eng. 48, 087006 (2009).
[Crossref]

Kane, J.

W. Pratt, J. Kane, H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57, 58–68 (1969).
[Crossref]

Kelly, K. F.

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

L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.

T. Goldstein, L. Xu, K. F. Kelly, R. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.3405 (2013).

Kittle, D.

Laska, J. N.

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

Li, Y.

L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.

Liao, X.

Liu, Z.

Z. Liu, A. Y. Elezzabi, H. V. Zhao, “Maximum frame rate video acquisition using adaptive compressed sensing,” IEEE Trans. Circuits Syst. Video Technol. 21, 1704–1718 (2011).
[Crossref]

Llull, P.

Marcia, R.

R. Marcia, R. M. Willett, “Compressive coded aperture video reconstruction,” in Proceedings of European Signal Processing Conference (EUSIPCO) (2008).

Mertz, J.

J. Mertz, Introduction to Optical Microscopy (Roberts, 2010).

Mirza, I. O.

Mousavi, H. S.

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

Mun, S.

J. E. Fowler, S. Mun, E. W. Tramel, “Block-based compressed sensing of images and video,” Found. Trends Signal Process. 4, 297–416 (2012).

Noor, I.

I. Noor, E. L. Jacobs, “Adaptive compressive sensing algorithm for video acquisition using a single-pixel camera,” J. Electron. Imaging 22, 021013 (2013).
[Crossref]

Pepperkok, R.

T. Zimmermann, J. Rietdorf, R. Pepperkok, “Spectral imaging and its applications in live cell microscopy,” FEBS Lett. 546, 87–92 (2003).
[Crossref]

Pitsianis, N.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Portnoy, A.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Prather, D. W.

Pratt, W.

W. Pratt, J. Kane, H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57, 58–68 (1969).
[Crossref]

Rietdorf, J.

T. Zimmermann, J. Rietdorf, R. Pepperkok, “Spectral imaging and its applications in live cell microscopy,” FEBS Lett. 546, 87–92 (2003).
[Crossref]

Romberg, J.

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

Sankaranarayanan, A.

L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.

Sankaranarayanan, A. C.

A. C. Sankaranarayanan, C. Studer, R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in International Conference on Computational Photography (ICCP) (IEEE, 2012).

Sapiro, G.

Sheppard, C. J. R.

C. J. R. Sheppard, T. Wilson, “Image formation in confocal scanning microscopes,” Optik 55, 331–342 (1980).

Studer, C.

A. C. Sankaranarayanan, C. Studer, R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in International Conference on Computational Photography (ICCP) (IEEE, 2012).

L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.

Studer, V.

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

Suleski, T.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Sun, T.

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

Sun, X.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Takhar, D.

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

Tao, T.

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

TeKolste, R.

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Tramel, E. W.

J. E. Fowler, S. Mun, E. W. Tramel, “Block-based compressed sensing of images and video,” Found. Trends Signal Process. 4, 297–416 (2012).

Wakin, M. B.

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

Willett, R. M.

R. Marcia, R. M. Willett, “Compressive coded aperture video reconstruction,” in Proceedings of European Signal Processing Conference (EUSIPCO) (2008).

Wilson, T.

C. J. R. Sheppard, T. Wilson, “Image formation in confocal scanning microscopes,” Optik 55, 331–342 (1980).

Wu, Y.

Xu, L.

T. Goldstein, L. Xu, K. F. Kelly, R. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.3405 (2013).

L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.

Yang, J.

Ye, P.

Yuan, X.

Zhao, H. V.

Z. Liu, A. Y. Elezzabi, H. V. Zhao, “Maximum frame rate video acquisition using adaptive compressed sensing,” IEEE Trans. Circuits Syst. Video Technol. 21, 1704–1718 (2011).
[Crossref]

Zheng, J.

J. Zheng, E. L. Jacobs, “Video compressive sensing using spatial domain sparsity,” Opt. Eng. 48, 087006 (2009).
[Crossref]

Zimmermann, T.

T. Zimmermann, J. Rietdorf, R. Pepperkok, “Spectral imaging and its applications in live cell microscopy,” FEBS Lett. 546, 87–92 (2003).
[Crossref]

FEBS Lett. (1)

T. Zimmermann, J. Rietdorf, R. Pepperkok, “Spectral imaging and its applications in live cell microscopy,” FEBS Lett. 546, 87–92 (2003).
[Crossref]

Found. Trends Signal Process. (1)

J. E. Fowler, S. Mun, E. W. Tramel, “Block-based compressed sensing of images and video,” Found. Trends Signal Process. 4, 297–416 (2012).

IEEE Signal Process. Mag. (2)

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

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

IEEE Trans. Circuits Syst. Video Technol. (1)

Z. Liu, A. Y. Elezzabi, H. V. Zhao, “Maximum frame rate video acquisition using adaptive compressed sensing,” IEEE Trans. Circuits Syst. Video Technol. 21, 1704–1718 (2011).
[Crossref]

IEEE Trans. Inf. Theory (2)

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

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

J. Electron. Imaging (1)

I. Noor, E. L. Jacobs, “Adaptive compressive sensing algorithm for video acquisition using a single-pixel camera,” J. Electron. Imaging 22, 021013 (2013).
[Crossref]

Opt. Eng. (1)

J. Zheng, E. L. Jacobs, “Video compressive sensing using spatial domain sparsity,” Opt. Eng. 48, 087006 (2009).
[Crossref]

Opt. Express (2)

Optik (1)

C. J. R. Sheppard, T. Wilson, “Image formation in confocal scanning microscopes,” Optik 55, 331–342 (1980).

Proc. IEEE (1)

W. Pratt, J. Kane, H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57, 58–68 (1969).
[Crossref]

Proc. Natl. Acad. Sci. USA (1)

V. Studer, J. Bobin, M. Chahid, H. S. Mousavi, E. Candes, M. Dahan, “Compressive fluorescence microscopy for biological and hyperspectral imaging,” Proc. Natl. Acad. Sci. USA 109, E1679–E1687 (2012).
[Crossref]

Proc. SPIE (1)

N. Pitsianis, D. Brady, A. Portnoy, X. Sun, T. Suleski, M. Fiddy, M. Feldman, R. TeKolste, “Compressive imaging sensors,” Proc. SPIE 6232, 62320A (2006).
[Crossref]

Other (5)

R. Marcia, R. M. Willett, “Compressive coded aperture video reconstruction,” in Proceedings of European Signal Processing Conference (EUSIPCO) (2008).

A. C. Sankaranarayanan, C. Studer, R. G. Baraniuk, “CS-MUVI: video compressive sensing for spatial-multiplexing cameras,” in International Conference on Computational Photography (ICCP) (IEEE, 2012).

T. Goldstein, L. Xu, K. F. Kelly, R. Baraniuk, “The STONE transform: multi-resolution image enhancement and real-time compressive video,” arXiv:1311.3405 (2013).

J. Mertz, Introduction to Optical Microscopy (Roberts, 2010).

L. Xu, A. Sankaranarayanan, C. Studer, Y. Li, R. G. Baraniuk, K. F. Kelly, “Multi-scale compressive video acquisition,” in Computational Optical Sensing and Imaging (Optical Society of America, 2013), paper CW2 C.4.

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Figures (5)

Fig. 1.
Fig. 1. Experimental setup and dual band images. (a) Sketch of the DMD operation. The input beam can be diverted, pixel by pixel, into one of two output arms. (b) Experimental setup. The diffuser (aperture) in the SWIR (visible) illumination arm can be removed (closed) to switch to a dark field configuration. (c) Reconstructed images from the visible and SWIR detectors of a silicon CMOS chip edge. The resolution is 128×128 and the images are the result of five averages and have been upscaled to 256×256 using linear interpolation.
Fig. 2.
Fig. 2. Optical modes. Comparison of bright- and dark-field operating modes. The visible and SWIR images were acquired simultaneously, but the sets of bright- and dark-field images were from separate acquisitions. The images are the result of five averages and have been upscaled to 256×256 using linear interpolation.
Fig. 3.
Fig. 3. Transmission images of a silicon CMOS chip captured in the SWIR band. The picture series shows a typical timeline when using sharpen mode. The resolution and averaging is controlled automatically based on the residuals. The line showing residuals is only for illustration and is not quantitatively accurate. The arrows indicate that the object in the scene is moving. The sample is entirely absorbing in the visible and thus no visible image is shown. All images have been upscaled to 256×256 using linear interpolation.
Fig. 4.
Fig. 4. Hadamard spectrum analysis and subset reconstruction. (a) Original image taken with a standard optical microscope. (b) Signal strength for each Hadamard pattern, ordered with largest signal first and plotted in log space for clarity. The lines are drawn at specific numbers of patterns and the number above is the percentage of total signal contained within that subset. (c) Numerical simulation reconstructions formed from subsets of patterns and signals. The subsets start with the largest signal and include ever smaller signals up to the number of patterns listed. The relative error is obtained from the absolute difference to a 4096 pattern reconstruction.
Fig. 5.
Fig. 5. Experimental results of the evolutionary mode. The images are taken simultaneously from the visible and SWIR channels, and the images are reconstructed from the number of patterns shown underneath. The images are the result of five averages and have been upscaled to 256×256 using linear interpolation.

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