Abstract

This Letter presents a new snapshot approach to hyperspectral imaging via dual-optical coding and compressive computational reconstruction. We demonstrate that two high-speed spatial light modulators, located conjugate to the image and spectral plane, respectively, can code the hyperspectral datacube into a single sensor image such that the high-resolution signal can be recovered in postprocessing. We show various applications by designing different optical modulation functions, including programmable spatially varying color filtering, multiplexed hyperspectral imaging, and high-resolution compressive hyperspectral imaging.

© 2014 Optical Society of America

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2013 (2)

2011 (2)

Y. Wu, I. O. Mirza, G. R. Arce, and D. W. Prather, Opt. Lett. 36, 2692 (2011).
[CrossRef]

E. J. Candes, Y. C. Eldar, D. Needell, and P. Randall, Appl. Comput. Harmon. Anal. 31, 59 (2011).
[CrossRef]

2010 (3)

2009 (1)

2008 (2)

E. van den Berg and M. P. Friedlander, SIAM J. Sci. Comput. 31, 890 (2008).
[CrossRef]

A. Mohan, R. Raskar, and J. Tumblin, Comput. Graph. Forum 27, 709 (2008).
[CrossRef]

2006 (3)

D. L. Donoho, IEEE Trans. Inf. Theory 52, 1289 (2006).
[CrossRef]

N. Gat, G. Scriven, J. Garman, M. D. Li, and J. Zhang, Proc. SPIE 6302, 63020M (2006).

M. Aharon, M. Elad, and A. Bruckstein, IEEE Trans. Signal Process. 54, 4311 (2006).
[CrossRef]

2005 (1)

E. J. Candes and T. Tao, IEEE Trans. Inf. Theory 51, 4203 (2005).
[CrossRef]

2001 (2)

D. L. Donoho and X. Huo, IEEE Trans. Inf. Theory 47, 2845 (2001).
[CrossRef]

B. K. Ford, M. R. Descour, and R. M. Lynch, Opt. Express 9, 444 (2001).
[CrossRef]

Aharon, M.

M. Aharon, M. Elad, and A. Bruckstein, IEEE Trans. Signal Process. 54, 4311 (2006).
[CrossRef]

Arce, G. R.

Arguello, H.

August, Y.

Brady, D. J.

Bruckstein, A.

M. Aharon, M. Elad, and A. Bruckstein, IEEE Trans. Signal Process. 54, 4311 (2006).
[CrossRef]

Candes, E. J.

E. J. Candes, Y. C. Eldar, D. Needell, and P. Randall, Appl. Comput. Harmon. Anal. 31, 59 (2011).
[CrossRef]

E. J. Candes and T. Tao, IEEE Trans. Inf. Theory 51, 4203 (2005).
[CrossRef]

Choi, K.

Descour, M. R.

Donoho, D. L.

D. L. Donoho, IEEE Trans. Inf. Theory 52, 1289 (2006).
[CrossRef]

D. L. Donoho and X. Huo, IEEE Trans. Inf. Theory 47, 2845 (2001).
[CrossRef]

Elad, M.

M. Aharon, M. Elad, and A. Bruckstein, IEEE Trans. Signal Process. 54, 4311 (2006).
[CrossRef]

Eldar, Y. C.

E. J. Candes, Y. C. Eldar, D. Needell, and P. Randall, Appl. Comput. Harmon. Anal. 31, 59 (2011).
[CrossRef]

Ford, B. K.

Friedlander, M. P.

E. van den Berg and M. P. Friedlander, SIAM J. Sci. Comput. 31, 890 (2008).
[CrossRef]

Gao, L.

Garman, J.

N. Gat, G. Scriven, J. Garman, M. D. Li, and J. Zhang, Proc. SPIE 6302, 63020M (2006).

Gat, N.

N. Gat, G. Scriven, J. Garman, M. D. Li, and J. Zhang, Proc. SPIE 6302, 63020M (2006).

Hagen, N.

Huo, X.

D. L. Donoho and X. Huo, IEEE Trans. Inf. Theory 47, 2845 (2001).
[CrossRef]

Iso, D.

F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, IEEE Trans. Image Process. 19, 2241 (2010).
[CrossRef]

Kester, R. T.

Kittle, D.

Li, M. D.

N. Gat, G. Scriven, J. Garman, M. D. Li, and J. Zhang, Proc. SPIE 6302, 63020M (2006).

Lynch, R. M.

Mirza, I. O.

Mitsunaga, T.

F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, IEEE Trans. Image Process. 19, 2241 (2010).
[CrossRef]

Mohan, A.

A. Mohan, R. Raskar, and J. Tumblin, Comput. Graph. Forum 27, 709 (2008).
[CrossRef]

Nayar, S. K.

F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, IEEE Trans. Image Process. 19, 2241 (2010).
[CrossRef]

Needell, D.

E. J. Candes, Y. C. Eldar, D. Needell, and P. Randall, Appl. Comput. Harmon. Anal. 31, 59 (2011).
[CrossRef]

Pitsianis, N. P.

Prather, D. W.

Randall, P.

E. J. Candes, Y. C. Eldar, D. Needell, and P. Randall, Appl. Comput. Harmon. Anal. 31, 59 (2011).
[CrossRef]

Raskar, R.

A. Mohan, R. Raskar, and J. Tumblin, Comput. Graph. Forum 27, 709 (2008).
[CrossRef]

Rivenson, Y.

Rueda, H.

Scriven, G.

N. Gat, G. Scriven, J. Garman, M. D. Li, and J. Zhang, Proc. SPIE 6302, 63020M (2006).

Stern, A.

Sun, X.

Tao, T.

E. J. Candes and T. Tao, IEEE Trans. Inf. Theory 51, 4203 (2005).
[CrossRef]

Tkaczyk, T. S.

Tumblin, J.

A. Mohan, R. Raskar, and J. Tumblin, Comput. Graph. Forum 27, 709 (2008).
[CrossRef]

Vachman, C.

van den Berg, E.

E. van den Berg and M. P. Friedlander, SIAM J. Sci. Comput. 31, 890 (2008).
[CrossRef]

Wagadarikar, A.

Wagadarikar, A. A.

Wu, Y.

Yasuma, F.

F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, IEEE Trans. Image Process. 19, 2241 (2010).
[CrossRef]

Zhang, J.

N. Gat, G. Scriven, J. Garman, M. D. Li, and J. Zhang, Proc. SPIE 6302, 63020M (2006).

Appl. Comput. Harmon. Anal. (1)

E. J. Candes, Y. C. Eldar, D. Needell, and P. Randall, Appl. Comput. Harmon. Anal. 31, 59 (2011).
[CrossRef]

Appl. Opt. (3)

Comput. Graph. Forum (1)

A. Mohan, R. Raskar, and J. Tumblin, Comput. Graph. Forum 27, 709 (2008).
[CrossRef]

IEEE Trans. Image Process. (1)

F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, IEEE Trans. Image Process. 19, 2241 (2010).
[CrossRef]

IEEE Trans. Inf. Theory (3)

D. L. Donoho, IEEE Trans. Inf. Theory 52, 1289 (2006).
[CrossRef]

E. J. Candes and T. Tao, IEEE Trans. Inf. Theory 51, 4203 (2005).
[CrossRef]

D. L. Donoho and X. Huo, IEEE Trans. Inf. Theory 47, 2845 (2001).
[CrossRef]

IEEE Trans. Signal Process. (1)

M. Aharon, M. Elad, and A. Bruckstein, IEEE Trans. Signal Process. 54, 4311 (2006).
[CrossRef]

Opt. Express (3)

Opt. Lett. (1)

Proc. SPIE (1)

N. Gat, G. Scriven, J. Garman, M. D. Li, and J. Zhang, Proc. SPIE 6302, 63020M (2006).

SIAM J. Sci. Comput. (1)

E. van den Berg and M. P. Friedlander, SIAM J. Sci. Comput. 31, 890 (2008).
[CrossRef]

Other (1)

www.cs.columbia.edu/CAVE/databases/multispectral/ .

Supplementary Material (1)

» Media 1: MOV (4053 KB)     

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Figures (8)

Fig. 1.
Fig. 1.

(a) Schematic of the DCSI system and (b) a photograph showing the prototype system.

Fig. 2.
Fig. 2.

Programmable spatially varying color filtering. A traditional Bayer color filter array, implemented with the proposed prototype, is shown in this example (Media 1).

Fig. 3.
Fig. 3.

Multiplexed hyperspectral imaging. In this example, an HS data cube with 16 spectral channels is reconstructed via demosaicing.

Fig. 4.
Fig. 4.

(a), (b) We quantitatively evaluate compressibility by transforming the HS datacube into different bases as well as the proposed over-complete dictionary. (c) Comparison of 3D HS images reconstructed from a single-coded 2D projection. (d) Sensitivity of the proposed sparse reconstruction algorithm w.r.t. to sensor noise.

Fig. 5.
Fig. 5.

Visualization of learned hyperspectral atoms in an over-completed dictionary.

Fig. 6.
Fig. 6.

Compressive hyperspectral imaging. An HS data cube containing a resolution chart with 31 spectral channels is reconstructed in this example.

Fig. 7.
Fig. 7.

Compressive HS imaging of a color checker scene. [(a), bottom row] and (b). From a randomly coded image, a high-resolution HS image with 31 waveband is reconstructed (Media 1). [(a), top]. A color-coded image of the reconstruction is shown. (c). We plot the retrieved spectrum of individual regions and compare them to ground-truth.

Fig. 8.
Fig. 8.

Comparison between coded aperture method (CASSI, top row) and proposed approach (bottom row).

Equations (6)

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