Abstract

We apply a coded aperture snapshot spectral imager (CASSI) to fluorescence microscopy. CASSI records a two-dimensional (2D) spectrally filtered projection of a three-dimensional (3D) spectral data cube. We minimize a convex quadratic function with total variation (TV) constraints for data cube estimation from the 2D snapshot. We adapt the TV minimization algorithm for direct fluorescent bead identification from CASSI measurements by combining a priori knowledge of the spectra associated with each bead type. Our proposed method creates a 2D bead identity image. Simulated fluorescence CASSI measurements are used to evaluate the behavior of the algorithm. We also record real CASSI measurements of a ten bead type fluorescence scene and create a 2D bead identity map. A baseline image from filtered-array imaging system verifies CASSI’s 2D bead identity map.

© 2010 Optical Society of America

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  1. J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
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    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef] [PubMed]
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    [CrossRef]
  19. E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489-509(2006).
    [CrossRef]
  20. J. Bioucas-Dias and M. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004(2007).
    [CrossRef] [PubMed]

2009 (2)

C. Fernandez, A. Wagadarikar, D. Brady, S. McCain, and T. Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” Proc. SPIE 7184, 71840Z (2009).
[CrossRef]

L. Gao, R. T. Kester, and T. Tkaczyk, “Compact image slicing spectrometer (iss) for hyperspectral fluorescence microscopy,” Opt. Express 17, 12293-12308 (2009).
[CrossRef] [PubMed]

2008 (2)

L. Li, X. Qi, X. Hai, and Z. Fa, “Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis,” Chin. Sci. Bull. 53, 1431-1434 (2008).
[CrossRef]

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

2007 (5)

W. Johnson, D. W. W. Fink, M. Humayun, and G. Bearman, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12, 014036 (2007).
[CrossRef] [PubMed]

R. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24(7), 118-121 (2007).
[CrossRef]

J. Bioucas-Dias and M. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004(2007).
[CrossRef] [PubMed]

S. Gebhart, R. Thompson, and A. Mahadevan-Jansen, “Liquid-crystal tunable filter spectral imaging for brain tumor demarcation,” Appl. Opt. 46, 1896-1910 (2007).
[CrossRef] [PubMed]

M. Gehm, R. John, D. Brady, R. Willet, and T. Schultz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15, 14013-14027 (2007).
[CrossRef] [PubMed]

2006 (3)

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

F. Woolfe, M. Maggioni, G. Davis, F. Warner, R. Coifman, and S. Zucker, “Hyperspectral microscopic analysis between normal, benign and carcinoma microarray tissue sections,” Proc. SPIE 6091, 60910I (2006).
[CrossRef]

M. Gehm, S. McCain, N. Pitsianis, D. Brady, P. Potuluri, and M. Sullivan, “Static two-dimensional aperture coding for multimodal, multiplex spectroscopy,” Appl. Opt. 45, 2965-2974(2006).
[CrossRef] [PubMed]

2005 (1)

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
[CrossRef]

2003 (1)

T. Vo-Dinh, B. Cullum, and P. Kasili, “Development of a multi-spectral imaging system for medical applications,” J. Phys. D 36, 1663-1668 (2003).
[CrossRef]

2002 (1)

M. L. Huebschman, R. A. Schultz, and H. R. Garner, “Characteristics and capabilities of the hyperspectral imaging microscope,” IEEE Eng. Med. Biol. Mag. 21, 104-117 (2002).
[CrossRef] [PubMed]

2001 (1)

C. Stellman, F. Olchowski, and J. Michalowicz, “WAR HORSE (wide-area reconnaissance: hyperspectral over-head real-time surveillance experiment,” Proc. SPIE 4379, 339-346 (2001).
[CrossRef]

2000 (1)

1996 (1)

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

1995 (1)

1985 (1)

A. Goetz, G. Vane, J. Solomon, and B. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147-1153(1985).
[CrossRef] [PubMed]

Altenbach, K.

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
[CrossRef]

Arlt, J.

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
[CrossRef]

Baraniuk, R.

R. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24(7), 118-121 (2007).
[CrossRef]

Bearman, G.

W. Johnson, D. W. W. Fink, M. Humayun, and G. Bearman, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12, 014036 (2007).
[CrossRef] [PubMed]

Bioucas-Dias, J.

J. Bioucas-Dias and M. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004(2007).
[CrossRef] [PubMed]

Bongiovi, R. P.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Brady, D.

Candes, E.

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

Chrien, T.

Coifman, R.

F. Woolfe, M. Maggioni, G. Davis, F. Warner, R. Coifman, and S. Zucker, “Hyperspectral microscopic analysis between normal, benign and carcinoma microarray tissue sections,” Proc. SPIE 6091, 60910I (2006).
[CrossRef]

Cullum, B.

T. Vo-Dinh, B. Cullum, and P. Kasili, “Development of a multi-spectral imaging system for medical applications,” J. Phys. D 36, 1663-1668 (2003).
[CrossRef]

Davis, G.

F. Woolfe, M. Maggioni, G. Davis, F. Warner, R. Coifman, and S. Zucker, “Hyperspectral microscopic analysis between normal, benign and carcinoma microarray tissue sections,” Proc. SPIE 6091, 60910I (2006).
[CrossRef]

Dereniak, E.

Descour, M.

Fa, Z.

L. Li, X. Qi, X. Hai, and Z. Fa, “Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis,” Chin. Sci. Bull. 53, 1431-1434 (2008).
[CrossRef]

Fernandez, C.

C. Fernandez, A. Wagadarikar, D. Brady, S. McCain, and T. Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” Proc. SPIE 7184, 71840Z (2009).
[CrossRef]

Figueiredo, M.

J. Bioucas-Dias and M. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004(2007).
[CrossRef] [PubMed]

Fink, D. W. W.

W. Johnson, D. W. W. Fink, M. Humayun, and G. Bearman, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12, 014036 (2007).
[CrossRef] [PubMed]

Fletcher-Holmes, D.

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
[CrossRef]

Gao, L.

Garner, H. R.

M. L. Huebschman, R. A. Schultz, and H. R. Garner, “Characteristics and capabilities of the hyperspectral imaging microscope,” IEEE Eng. Med. Biol. Mag. 21, 104-117 (2002).
[CrossRef] [PubMed]

Gebhart, S.

Gehm, M.

Goetz, A.

A. Goetz, G. Vane, J. Solomon, and B. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147-1153(1985).
[CrossRef] [PubMed]

Gorman, A.

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
[CrossRef]

Green, R.

Haaland, D.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Hackwell, J.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Hai, X.

L. Li, X. Qi, X. Hai, and Z. Fa, “Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis,” Chin. Sci. Bull. 53, 1431-1434 (2008).
[CrossRef]

Hamad, S.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Hansel, S. J.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Harvey, A.

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
[CrossRef]

Hayhurst, T.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Huebschman, M. L.

M. L. Huebschman, R. A. Schultz, and H. R. Garner, “Characteristics and capabilities of the hyperspectral imaging microscope,” IEEE Eng. Med. Biol. Mag. 21, 104-117 (2002).
[CrossRef] [PubMed]

Humayun, M.

W. Johnson, D. W. W. Fink, M. Humayun, and G. Bearman, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12, 014036 (2007).
[CrossRef] [PubMed]

John, R.

Johnson, W.

W. Johnson, D. W. W. Fink, M. Humayun, and G. Bearman, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12, 014036 (2007).
[CrossRef] [PubMed]

Jones, H.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Kasili, P.

T. Vo-Dinh, B. Cullum, and P. Kasili, “Development of a multi-spectral imaging system for medical applications,” J. Phys. D 36, 1663-1668 (2003).
[CrossRef]

Kester, R. T.

Li, L.

L. Li, X. Qi, X. Hai, and Z. Fa, “Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis,” Chin. Sci. Bull. 53, 1431-1434 (2008).
[CrossRef]

Mabry, D.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Maggioni, M.

F. Woolfe, M. Maggioni, G. Davis, F. Warner, R. Coifman, and S. Zucker, “Hyperspectral microscopic analysis between normal, benign and carcinoma microarray tissue sections,” Proc. SPIE 6091, 60910I (2006).
[CrossRef]

Mahadevan-Jansen, A.

McCain, S.

C. Fernandez, A. Wagadarikar, D. Brady, S. McCain, and T. Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” Proc. SPIE 7184, 71840Z (2009).
[CrossRef]

M. Gehm, S. McCain, N. Pitsianis, D. Brady, P. Potuluri, and M. Sullivan, “Static two-dimensional aperture coding for multimodal, multiplex spectroscopy,” Appl. Opt. 45, 2965-2974(2006).
[CrossRef] [PubMed]

Melgaard, D.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Michalowicz, J.

C. Stellman, F. Olchowski, and J. Michalowicz, “WAR HORSE (wide-area reconnaissance: hyperspectral over-head real-time surveillance experiment,” Proc. SPIE 4379, 339-346 (2001).
[CrossRef]

Mouroulis, P.

Nieman, L.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Olchowski, F.

C. Stellman, F. Olchowski, and J. Michalowicz, “WAR HORSE (wide-area reconnaissance: hyperspectral over-head real-time surveillance experiment,” Proc. SPIE 4379, 339-346 (2001).
[CrossRef]

Oliver, T.

C. Fernandez, A. Wagadarikar, D. Brady, S. McCain, and T. Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” Proc. SPIE 7184, 71840Z (2009).
[CrossRef]

Pitsianis, N.

Potuluri, P.

Qi, X.

L. Li, X. Qi, X. Hai, and Z. Fa, “Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis,” Chin. Sci. Bull. 53, 1431-1434 (2008).
[CrossRef]

Read, N.

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
[CrossRef]

Rock, B.

A. Goetz, G. Vane, J. Solomon, and B. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147-1153(1985).
[CrossRef] [PubMed]

Romberg, J.

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

Schultz, R. A.

M. L. Huebschman, R. A. Schultz, and H. R. Garner, “Characteristics and capabilities of the hyperspectral imaging microscope,” IEEE Eng. Med. Biol. Mag. 21, 104-117 (2002).
[CrossRef] [PubMed]

Schultz, T.

Sinclair, M.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Sivjee, M.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Skinner, J.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Solomon, J.

A. Goetz, G. Vane, J. Solomon, and B. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147-1153(1985).
[CrossRef] [PubMed]

Stellman, C.

C. Stellman, F. Olchowski, and J. Michalowicz, “WAR HORSE (wide-area reconnaissance: hyperspectral over-head real-time surveillance experiment,” Proc. SPIE 4379, 339-346 (2001).
[CrossRef]

Sullivan, M.

Tao, T.

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

Thompson, R.

Timlin, J.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Tkaczyk, T.

Vane, G.

A. Goetz, G. Vane, J. Solomon, and B. Rock, “Imaging spectrometry for earth remote sensing,” Science 228, 1147-1153(1985).
[CrossRef] [PubMed]

Vermaas, W.

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Vo-Dinh, T.

T. Vo-Dinh, B. Cullum, and P. Kasili, “Development of a multi-spectral imaging system for medical applications,” J. Phys. D 36, 1663-1668 (2003).
[CrossRef]

Wagadarikar, A.

C. Fernandez, A. Wagadarikar, D. Brady, S. McCain, and T. Oliver, “Fluorescence microscopy with a coded aperture snapshot spectral imager,” Proc. SPIE 7184, 71840Z (2009).
[CrossRef]

Warner, F.

F. Woolfe, M. Maggioni, G. Davis, F. Warner, R. Coifman, and S. Zucker, “Hyperspectral microscopic analysis between normal, benign and carcinoma microarray tissue sections,” Proc. SPIE 6091, 60910I (2006).
[CrossRef]

Warren, D. W.

J. Hackwell, D. W. Warren, R. P. Bongiovi, S. J. Hansel, T. Hayhurst, D. Mabry, M. Sivjee, and J. Skinner, “LWIR/MWIR imaging hyperspectral sensor for airborne and ground-based remote sensing,” Proc. SPIE 2819, 102-107 (1996).
[CrossRef]

Willet, R.

Woolfe, F.

F. Woolfe, M. Maggioni, G. Davis, F. Warner, R. Coifman, and S. Zucker, “Hyperspectral microscopic analysis between normal, benign and carcinoma microarray tissue sections,” Proc. SPIE 6091, 60910I (2006).
[CrossRef]

Zucker, S.

F. Woolfe, M. Maggioni, G. Davis, F. Warner, R. Coifman, and S. Zucker, “Hyperspectral microscopic analysis between normal, benign and carcinoma microarray tissue sections,” Proc. SPIE 6091, 60910I (2006).
[CrossRef]

Appl. Opt. (4)

Chin. Sci. Bull. (1)

L. Li, X. Qi, X. Hai, and Z. Fa, “Study on microscope hyperspectral medical imaging method for biomedical quantitative analysis,” Chin. Sci. Bull. 53, 1431-1434 (2008).
[CrossRef]

IEEE Eng. Med. Biol. Mag. (1)

M. L. Huebschman, R. A. Schultz, and H. R. Garner, “Characteristics and capabilities of the hyperspectral imaging microscope,” IEEE Eng. Med. Biol. Mag. 21, 104-117 (2002).
[CrossRef] [PubMed]

IEEE Signal Process. Mag. (1)

R. Baraniuk, “Compressive sensing,” IEEE Signal Process. Mag. 24(7), 118-121 (2007).
[CrossRef]

IEEE Trans. Image Process. (1)

J. Bioucas-Dias and M. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16, 2992-3004(2007).
[CrossRef] [PubMed]

IEEE Trans. Inf. Theory (1)

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

J. Biomed. Opt. (1)

W. Johnson, D. W. W. Fink, M. Humayun, and G. Bearman, “Snapshot hyperspectral imaging in ophthalmology,” J. Biomed. Opt. 12, 014036 (2007).
[CrossRef] [PubMed]

J. Phys. D (1)

T. Vo-Dinh, B. Cullum, and P. Kasili, “Development of a multi-spectral imaging system for medical applications,” J. Phys. D 36, 1663-1668 (2003).
[CrossRef]

Opt. Express (2)

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

W. Vermaas, J. Timlin, H. Jones, M. Sinclair, L. Nieman, S. Hamad, D. Melgaard, and D. Haaland, “In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells,” Proc. Natl. Acad. Sci. USA 105, 4050-4055 (2008).
[CrossRef] [PubMed]

Proc. SPIE (5)

A. Harvey, D. Fletcher-Holmes, A. Gorman, K. Altenbach, J. Arlt, and N. Read, “Spectral imaging in a snapshot,” Proc. SPIE 5694, 110-119 (2005).
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Figures (7)

Fig. 1
Fig. 1

(a) Optical architecture for a dual disperser CASSI consisting of Amici prisms (AP1 and AP2), an aperture code (M) and a focal plane (FP). (b) Power spectral density profile propagated through the system optical architecture. The effect of the aperture code on the power spectral density is illustrated.

Fig. 2
Fig. 2

(a) Object data cube (f) transformation into a sparse data cube (α) using spectral priors (W). A spectrum recorded at a single pixel location in f corresponds to a single pixel value/bead identity in α. (b) Matrix representation of the spectral data base, W.

Fig. 3
Fig. 3

(a) Simulated 64 × 64 pixel aperture code. (b) Downsampled fluorescence spectra of a 0.3 intensity-valued yellow green ( YG / n = 1 ), 0.5 intensity-valued orange ( O / n = 2 ), 1.0 intensity-valued red ( R / n = 3 ) and 0.7 intensity-valued crimson ( C / n = 4 ) beads. (c) Simulated 10 × 10 pixel fluorescent squares in a 64 × 64 pixel image with the corresponding (d) simulated detector image. (e) Input image for 15 × 15 pixel spectrally different fluorescent squares in a 64 × 64 pixel image with the corresponding (f) simulated detector image.

Fig. 4
Fig. 4

Simulated α and reconstructed α * data cube, where each n channel relates to a single spectral vector in W. (YG, n = 1 ; O, n = 2 ; R, n = 3 ; C, n = 4 ) (a) “true” α ( m 1 , m 2 ) as a function of n for 10 × 10 pixel squares (b) estimated α * ( m 1 , m 2 ) as a function of n for 10 × 10 pixel squares. The dotted line in the n = 4 slice represents a residual artifact from the n = 3 slice. (c) “true” α ( m 1 , m 2 ) as a function of n for 15 × 15 pixel squares (d) estimated α * ( m 1 , m 2 ) as a function of n for 15 × 15 pixel squares.

Fig. 5
Fig. 5

Plot of reconstruction MSE from CASSI measurements corrupted by Poisson noise. Reconstruction efficacy is compared between (a) direct f * data cube estimation and (b)  f α * data cube estimation (see Subsection 3C for the definition of f α * ).

Fig. 6
Fig. 6

(a)  Optical architecture for a CASSI interface to an inverted microscope. (b) Realization of Fig. 1a is in this ray-traced drawing for the first half of CASSI where (f) is the object ( L 1 ) and ( L 2 ) are imaging and collimating lenses, (AP) is a direct-view double Amici prism and (MP) is the mask plane where the aperture code resides. (c) Layout of a Zeiss AxioObserver microscope with CASSI coupled to an exit port. (d) Back end of CASSI.

Fig. 7
Fig. 7

(a) Baseline CASSI 2D intensity-valued measurement of fluorescent microsphere scenes acquired with a 50 × , 0.4 NA microscope objective. White circles are added to the images to outline the locations of the beads. (b) CASSI reconstructed 2D spectral feature map, γ 1 * . (c) CASSI 2D spectral fea ture map, γ 2 * . (d) Nikon A1 series baseline image with ten bead type discrimination where beads are additionally outlined in white. (e) Spectral vectors used in the database, W, for CASSI reconstructions.

Tables (1)

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Table 1 Fluorescent Microspheres

Equations (33)

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f 1 ( x , y , λ ) = f 0 ( x , y , λ ) h 1 ( x , x , y , y , λ ) d x d y .
h 1 ( x , x , y , y , λ ) = δ ( x [ x + ξ 1 ( λ λ C ) ] ) δ ( y y ) .
f 1 ( x , y , λ ) = f 0 ( x , y , λ ) δ ( x [ x + ξ 1 ( λ λ C ) ] ) δ ( y y ) d x d y
= f 0 ( x + ξ 1 ( λ λ C ) , y , λ ) .
f 2 ( x , y , λ ) = t ( x , y ) f 0 ( x + ξ 1 ( λ λ C ) , y , λ ) ,
t ( x , y ) = i j N t i j rect ( x i Δ T Δ T , y j Δ T Δ T ) ,
f 3 ( x , y , λ ) = f 2 ( x , y , λ ) h 2 ( x , x , y , y , λ ) d x d y ,
f 3 ( x , y , λ ) = t ( x , y ) f 0 ( x + ξ 1 ( λ λ C ) , y , λ ) h 2 ( x , x , y , y , λ ) d x d y .
h 2 ( x , x , y , y ) = δ ( x [ x + ξ 2 ( λ λ C ) ] ) δ ( y y ) .
f 3 ( x , y , λ ) = f 0 ( x , y , λ ) t ( x ξ 1 ( λ λ C ) , y ) .
g ( x , y ) = f 0 ( x , y , λ ) H ( x , y , λ ) d λ .
g m n = t ( x ξ 1 ( λ λ C ) , y , λ ) f 0 ( x , y , λ ) p m n ( x , y ) d x d y d λ ,
p m n ( x , y ) = rect ( x m Δ Δ , y n Δ Δ ) ,
g m n = i j t i j rect ( x ξ 1 ( λ λ C ) i Δ T Δ T , y j Δ T Δ T ) rect ( x m Δ Δ , y n Δ Δ ) f 0 ( x , y , λ ) d x d y d λ .
g m n = i j t i j rect [ x ( k i ) Δ Δ , y j Δ Δ ] rect [ x m Δ Δ , y n Δ Δ ] f 0 ( x , y , λ ) d x d y d λ .
g m n = i j t i j δ i , k m δ j , n f 0 ( x + m Δ , y + n Δ , λ + k Δ ) rect [ x Δ , y Δ ] d x d y d λ ,
f m , n , k = f 0 ( x + m Δ , y + n Δ , λ + k Δ ) rect [ x Δ , y Δ ] δ ( λ + k Δ ) d x d y d λ .
g m n = i j k t i , j f m , n , k δ i , k m δ j , n ,
g m n = k t k m , n f m , n , k .
g = H f .
f * = arg min f g H f 2 2 + τ Φ TV ( f ) ,
Φ TV ( f ) = k i , j ( f i + 1 , j , k f i , j , k ) 2 + ( f i , j + 1 , k f i , j , k ) 2 .
f = W α ,
g = H W α ,
( H W ) T = W T C T V T .
α * = arg min α g H W α 2 2 + τ Φ TV ( α ) ,
β ( m 1 , m 2 , n ) = max n α * ( m 1 , m 2 , n ) .
γ 1 * ( m 1 , m 2 ) = n γ 1 ( m 1 , m 2 , n ) .
γ 2 ( m 1 , m 2 , n ) = n [ β ( m 1 , m 2 , n ) max n β ( m 1 , m 2 , n ) ] .
γ 2 * ( m 1 , m 2 ) = n γ 2 ( m 1 , m 2 , n ) .
f i , j , k = ( f i + 1 , j , k f i , j , k ) 2 + ( f i , j + 1 , k f i , j , k ) 2 .
SNR = def 10 log 10 { m n g m , n 2 m n ( g m , n p g m , n ) 2 } ,
g m , n p 1 η Poisson { η g m , n } .

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