Abstract

In various applications, such as remote sensing and quality inspection, hyperspectral (HS) imaging is performed by spatially scanning an object. In this work, we present a new compressive hyperspectral imaging method that performs along-track scanning. The method relies on the compressive sensing miniature ultra-spectral imaging (CS-MUSI) system, which uses a single liquid crystal (LC) cell for spectral encoding and provides a more efficient way of HS data acquisition, compared to classical spatial scanning based systems. The experimental results show that a compression ratio of about 1:10 can be reached. Owing to the inherent compression, the captured data is preprepared for efficient storage and transmission.

© 2016 Optical Society of America

Full Article  |  PDF Article
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References

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2016 (2)

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder,” Sci. Rep. 6, 23524 (2016).

Y. Oiknine, I. August, L. Revah, and A. Stern, “Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system,” Proc. SPIE 9857, 9857 (2016).

2014 (3)

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).
[Crossref]

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graph. 33(6), 233 (2014).
[Crossref]

R. M. Willett, M. F. Duarte, M. Davenport, and R. G. Baraniuk, “Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection,” IEEE Signal Process. Mag. 31(1), 116–126 (2014).
[Crossref]

2013 (3)

2012 (1)

C. Li, T. Sun, K. F. Kelly, and Y. Zhang, “A compressive sensing and unmixing scheme for hyperspectral data processing,” IEEE Trans. Image Process. 21(3), 1200–1210 (2012).
[Crossref] [PubMed]

2011 (1)

S. Becker, J. Bobin, and E. J. Candès, “NESTA: a fast and accurate first-order method for sparse recovery,” SIAM J. Imaging Sci. 4(1), 1–39 (2011).
[Crossref]

2010 (1)

P. W. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).
[Crossref]

2009 (2)

S. J. Wright, R. D. Nowak, and M. A. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57(7), 2479–2493 (2009).
[Crossref]

A. A. Wagadarikar, N. P. Pitsianis, X. Sun, and D. J. Brady, “Video rate spectral imaging using a coded aperture snapshot spectral imager,” Opt. Express 17(8), 6368–6388 (2009).
[Crossref] [PubMed]

2007 (3)

M. E. Gehm, R. John, D. J. Brady, R. M. Willett, and T. J. Schulz, “Single-shot compressive spectral imaging with a dual-disperser architecture,” Opt. Express 15(21), 14013–14027 (2007).
[Crossref] [PubMed]

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
[Crossref] [PubMed]

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).
[Crossref]

2006 (2)

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).
[Crossref]

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

2005 (1)

S. Usama, M. Montaser, and O. Ahmed, “A complexity and quality evaluation of block based motion estimation algorithms,” Acta Polytech. 45, 1-13 (2005).

1993 (1)

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
[Crossref]

1985 (1)

R. Carlson and F. Fritsch, “Monotone piecewise bicubic interpolation,” SIAM J. Numer. Anal. 22(2), 386–400 (1985).
[Crossref]

Abdulhalim, I.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder,” Sci. Rep. 6, 23524 (2016).

AbuLeil, M.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder,” Sci. Rep. 6, 23524 (2016).

Ahmed, O.

S. Usama, M. Montaser, and O. Ahmed, “A complexity and quality evaluation of block based motion estimation algorithms,” Acta Polytech. 45, 1-13 (2005).

Arce, G. R.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).
[Crossref]

Arguello, H.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).
[Crossref]

August, I.

Y. Oiknine, I. August, L. Revah, and A. Stern, “Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system,” Proc. SPIE 9857, 9857 (2016).

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder,” Sci. Rep. 6, 23524 (2016).

August, Y.

Baraniuk, R. G.

R. M. Willett, M. F. Duarte, M. Davenport, and R. G. Baraniuk, “Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection,” IEEE Signal Process. Mag. 31(1), 116–126 (2014).
[Crossref]

Becker, S.

S. Becker, J. Bobin, and E. J. Candès, “NESTA: a fast and accurate first-order method for sparse recovery,” SIAM J. Imaging Sci. 4(1), 1–39 (2011).
[Crossref]

Bilal, R. M.

S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee, “Compressive sensing: From theory to applications, a survey,” J. Commun. Netw. (Seoul) 15(5), 443–456 (2013).
[Crossref]

Bin, W.

B. Xiaojia, M. Fang, W. Bin, L. Jiaguang, and W. Dong, “Hyperion hyperspectral remote sensing application in altered mineral mapping in East Kunlun of the Qinghai-Tibet Plateau,” in 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE) (IEEE, 2010), pp. 519–523.
[Crossref]

Bioucas-Dias, J. M.

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
[Crossref] [PubMed]

Bobin, J.

S. Becker, J. Bobin, and E. J. Candès, “NESTA: a fast and accurate first-order method for sparse recovery,” SIAM J. Imaging Sci. 4(1), 1–39 (2011).
[Crossref]

Brady, D. J.

Candès, E. J.

S. Becker, J. Bobin, and E. J. Candès, “NESTA: a fast and accurate first-order method for sparse recovery,” SIAM J. Imaging Sci. 4(1), 1–39 (2011).
[Crossref]

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

Carin, L.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).
[Crossref]

Carlson, R.

R. Carlson and F. Fritsch, “Monotone piecewise bicubic interpolation,” SIAM J. Numer. Anal. 22(2), 386–400 (1985).
[Crossref]

Cetin, H.

H. Cetin, J. Pafford, and T. Mueller, “Precision agriculture using hyperspectral remote sensing and GIS,” in Proceedings of 2nd International Conference on Recent Advances in Space Technologies, RAST 2005 (IEEE, 2005), pp. 70–77.
[Crossref]

Chrien, T. G.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
[Crossref]

Dai, Q.

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graph. 33(6), 233 (2014).
[Crossref]

Davenport, M.

R. M. Willett, M. F. Duarte, M. Davenport, and R. G. Baraniuk, “Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection,” IEEE Signal Process. Mag. 31(1), 116–126 (2014).
[Crossref]

Dong, W.

B. Xiaojia, M. Fang, W. Bin, L. Jiaguang, and W. Dong, “Hyperion hyperspectral remote sensing application in altered mineral mapping in East Kunlun of the Qinghai-Tibet Plateau,” in 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE) (IEEE, 2010), pp. 519–523.
[Crossref]

Donoho, D. L.

D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).
[Crossref]

Duarte, M. F.

R. M. Willett, M. F. Duarte, M. Davenport, and R. G. Baraniuk, “Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection,” IEEE Signal Process. Mag. 31(1), 116–126 (2014).
[Crossref]

Enmark, H. T.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
[Crossref]

Fang, M.

B. Xiaojia, M. Fang, W. Bin, L. Jiaguang, and W. Dong, “Hyperion hyperspectral remote sensing application in altered mineral mapping in East Kunlun of the Qinghai-Tibet Plateau,” in 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE) (IEEE, 2010), pp. 519–523.
[Crossref]

Figueiredo, M. A.

S. J. Wright, R. D. Nowak, and M. A. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57(7), 2479–2493 (2009).
[Crossref]

J. M. Bioucas-Dias and M. A. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
[Crossref] [PubMed]

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).
[Crossref]

Fritsch, F.

R. Carlson and F. Fritsch, “Monotone piecewise bicubic interpolation,” SIAM J. Numer. Anal. 22(2), 386–400 (1985).
[Crossref]

Gehm, M. E.

Green, R. O.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
[Crossref]

Hansen, E. G.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
[Crossref]

Iqbal, W.

S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee, “Compressive sensing: From theory to applications, a survey,” J. Commun. Netw. (Seoul) 15(5), 443–456 (2013).
[Crossref]

Jiaguang, L.

B. Xiaojia, M. Fang, W. Bin, L. Jiaguang, and W. Dong, “Hyperion hyperspectral remote sensing application in altered mineral mapping in East Kunlun of the Qinghai-Tibet Plateau,” in 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE) (IEEE, 2010), pp. 519–523.
[Crossref]

John, R.

Kelly, K. F.

C. Li, T. Sun, K. F. Kelly, and Y. Zhang, “A compressive sensing and unmixing scheme for hyperspectral data processing,” IEEE Trans. Image Process. 21(3), 1200–1210 (2012).
[Crossref] [PubMed]

Kittle, D. S.

G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).
[Crossref]

Lee, S.

S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee, “Compressive sensing: From theory to applications, a survey,” J. Commun. Netw. (Seoul) 15(5), 443–456 (2013).
[Crossref]

Li, C.

C. Li, T. Sun, K. F. Kelly, and Y. Zhang, “A compressive sensing and unmixing scheme for hyperspectral data processing,” IEEE Trans. Image Process. 21(3), 1200–1210 (2012).
[Crossref] [PubMed]

Lin, X.

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graph. 33(6), 233 (2014).
[Crossref]

Liu, Y.

X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graph. 33(6), 233 (2014).
[Crossref]

Montaser, M.

S. Usama, M. Montaser, and O. Ahmed, “A complexity and quality evaluation of block based motion estimation algorithms,” Acta Polytech. 45, 1-13 (2005).

Mueller, T.

H. Cetin, J. Pafford, and T. Mueller, “Precision agriculture using hyperspectral remote sensing and GIS,” in Proceedings of 2nd International Conference on Recent Advances in Space Technologies, RAST 2005 (IEEE, 2005), pp. 70–77.
[Crossref]

Naureen, M.

S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee, “Compressive sensing: From theory to applications, a survey,” J. Commun. Netw. (Seoul) 15(5), 443–456 (2013).
[Crossref]

Nowak, R. D.

S. J. Wright, R. D. Nowak, and M. A. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57(7), 2479–2493 (2009).
[Crossref]

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).
[Crossref]

Oiknine, Y.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder,” Sci. Rep. 6, 23524 (2016).

Y. Oiknine, I. August, L. Revah, and A. Stern, “Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system,” Proc. SPIE 9857, 9857 (2016).

Pafford, J.

H. Cetin, J. Pafford, and T. Mueller, “Precision agriculture using hyperspectral remote sensing and GIS,” in Proceedings of 2nd International Conference on Recent Advances in Space Technologies, RAST 2005 (IEEE, 2005), pp. 70–77.
[Crossref]

Pitsianis, N. P.

Porter, W. M.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
[Crossref]

Qaisar, S.

S. Qaisar, R. M. Bilal, W. Iqbal, M. Naureen, and S. Lee, “Compressive sensing: From theory to applications, a survey,” J. Commun. Netw. (Seoul) 15(5), 443–456 (2013).
[Crossref]

Revah, L.

Y. Oiknine, I. August, L. Revah, and A. Stern, “Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system,” Proc. SPIE 9857, 9857 (2016).

Richardson, M.

P. W. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).
[Crossref]

Rivenson, Y.

Romberg, J.

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

Schulz, T. J.

Stern, A.

Y. Oiknine, I. August, L. Revah, and A. Stern, “Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system,” Proc. SPIE 9857, 9857 (2016).

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder,” Sci. Rep. 6, 23524 (2016).

Y. August and A. Stern, “Compressive sensing spectrometry based on liquid crystal devices,” Opt. Lett. 38(23), 4996–4999 (2013).
[Crossref] [PubMed]

Y. August, C. Vachman, Y. Rivenson, and A. Stern, “Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains,” Appl. Opt. 52(10), D46–D54 (2013).
[Crossref] [PubMed]

Y. August, C. Vachman, and A. Stern, “Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging,” in SPIE Defense, Security, and Sensing (ISOP, 2013), pp. 87170G.

Sun, T.

C. Li, T. Sun, K. F. Kelly, and Y. Zhang, “A compressive sensing and unmixing scheme for hyperspectral data processing,” IEEE Trans. Image Process. 21(3), 1200–1210 (2012).
[Crossref] [PubMed]

Sun, X.

Tao, T.

E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52(2), 489–509 (2006).
[Crossref]

Usama, S.

S. Usama, M. Montaser, and O. Ahmed, “A complexity and quality evaluation of block based motion estimation algorithms,” Acta Polytech. 45, 1-13 (2005).

Vachman, C.

Y. August, C. Vachman, Y. Rivenson, and A. Stern, “Compressive hyperspectral imaging by random separable projections in both the spatial and the spectral domains,” Appl. Opt. 52(10), D46–D54 (2013).
[Crossref] [PubMed]

Y. August, C. Vachman, and A. Stern, “Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging,” in SPIE Defense, Security, and Sensing (ISOP, 2013), pp. 87170G.

Vane, G.

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
[Crossref]

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M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).
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X. Lin, Y. Liu, J. Wu, and Q. Dai, “Spatial-spectral encoded compressive hyperspectral imaging,” ACM Trans. Graph. 33(6), 233 (2014).
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B. Xiaojia, M. Fang, W. Bin, L. Jiaguang, and W. Dong, “Hyperion hyperspectral remote sensing application in altered mineral mapping in East Kunlun of the Qinghai-Tibet Plateau,” in 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE) (IEEE, 2010), pp. 519–523.
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P. W. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).
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C. Li, T. Sun, K. F. Kelly, and Y. Zhang, “A compressive sensing and unmixing scheme for hyperspectral data processing,” IEEE Trans. Image Process. 21(3), 1200–1210 (2012).
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Appl. Opt. (1)

IEEE J. Sel. Top. Signal Process. (1)

M. A. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1(4), 586–597 (2007).
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IEEE Signal Process. Mag. (2)

R. M. Willett, M. F. Duarte, M. Davenport, and R. G. Baraniuk, “Sparsity and structure in hyperspectral imaging: Sensing, reconstruction, and target detection,” IEEE Signal Process. Mag. 31(1), 116–126 (2014).
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G. R. Arce, D. J. Brady, L. Carin, H. Arguello, and D. S. Kittle, “Compressive coded aperture spectral imaging: An introduction,” IEEE Signal Process. Mag. 31(1), 105–115 (2014).
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IEEE Trans. Image Process. (2)

C. Li, T. Sun, K. F. Kelly, and Y. Zhang, “A compressive sensing and unmixing scheme for hyperspectral data processing,” IEEE Trans. Image Process. 21(3), 1200–1210 (2012).
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S. J. Wright, R. D. Nowak, and M. A. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57(7), 2479–2493 (2009).
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Imaging Sci. J. (1)

P. W. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).
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Opt. Express (2)

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Proc. SPIE (1)

Y. Oiknine, I. August, L. Revah, and A. Stern, “Comparison between various patch wise strategies for reconstruction of ultra-spectral cubes captured with a compressive sensing system,” Proc. SPIE 9857, 9857 (2016).

Remote Sens. Environ. (1)

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen, and W. M. Porter, “The airborne visible/infrared imaging spectrometer (AVIRIS),” Remote Sens. Environ. 44(2-3), 127–143 (1993).
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I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature Compressive Ultra-spectral Imaging System Utilizing a Single Liquid Crystal Phase Retarder,” Sci. Rep. 6, 23524 (2016).

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B. Xiaojia, M. Fang, W. Bin, L. Jiaguang, and W. Dong, “Hyperion hyperspectral remote sensing application in altered mineral mapping in East Kunlun of the Qinghai-Tibet Plateau,” in 2010 International Conference on Challenges in Environmental Science and Computer Engineering (CESCE) (IEEE, 2010), pp. 519–523.
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Y. August, C. Vachman, and A. Stern, “Spatial versus spectral compression ratio in compressive sensing of hyperspectral imaging,” in SPIE Defense, Security, and Sensing (ISOP, 2013), pp. 87170G.

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Figures (9)

Fig. 1
Fig. 1 (a) Hyperspectral cube data, (b) captured with two exposures of spatial scanning (push broom) and (c) with two exposures using CS-MUSI spectral scanning process. The figures show heuristically the parts of the datacube sampled at two different exposures. The spectrally encoded data is integrated pixelwise.
Fig. 2
Fig. 2 CS-MUSI camera optical design with the LC located at the field stop.
Fig. 3
Fig. 3 Along-track scanning with the CS-MUSI system. At every exposure the CS-MUSI camera captures a scene, f ( t ) , with a different spectral multiplexing depending on the voltage applied on the LC, v t .
Fig. 4
Fig. 4 Left: Image of the sensing matrix Φ n,m showing the spectral transmission as a function of voltage/exposure. Each row in the matrix represents the spectral transmission for voltage v p . Center and Right: Rearranged matrices for two different pixels in two different columns of the scene (vertical strips in the object grid).
Fig. 5
Fig. 5 Set of voltages applied to the LC as a function of exposure number, t, applied in a perodic way (left) and set of voltages as a function of exposure number, t, and the strip exposure number [i from Eq. (8)] for a specific vertical strip (right). In this example, there are M λ =100 different voltages.
Fig. 6
Fig. 6 Captured 2D images, g ( t ) , before and after registration process.
Fig. 7
Fig. 7 Displacement between consecutive shots as a function of exposure number; where the upper graph is in the scanning direction (horizontal translation) and the lower graph is in the axis vertical to the scanning (vertical translation).
Fig. 8
Fig. 8 (a) Original image (b) RGB representation of the HS cube in the scanning CS-MUSI experiment.
Fig. 9
Fig. 9 Reconstruction results at five different wavelengths; 460, 488, 520, 590 and 630nm.

Equations (11)

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g=Φf,
min f ˜ {    gΦ f ˜ 2 2 +τ f ˜ 1   },
T( λ ) 1 2 1 2 cos( dπΔn(V) λ ),
g t ( x,y )= ϕ v t ( λ )rect( x L x , y L y )f( x+( t1 ) d x ,y,λ )dλ ,
g n,m ( t ) = ϕ v t ( λ )rect( x L x , y L y )f( x+( t1 ) d x ,y,λ )rect( x Δ n, y Δ m )dλ ,
g n,m ( t ) = k ϕ v t ,k f n+l,m,k ,
f ( t ) =( f l,0 f l+1,0 f l+ L x /Δ ,0 f l,1 f l+1,1 f l+ L x /Δ ,1 f l, L y /Δ f l+1, L y /Δ f l+ L x /Δ , L y /Δ ),
f n,m { g n 1 ,m ( p 1 ) g n 2 ,m ( p 2 ) g n M ,m ( p M ) },
Φ n,m = [ ϕ v p 1 ϕ v p 2 ϕ v p M ] T ,
Φ s = 1 N y N s × N y N s Φ n 1 s ,m =[ [ Φ n 1 s ,m ] 0 M λ × N λ 0 M λ × N λ 0 M λ × N λ [ Φ n 1 s ,m ] 0 M λ × N λ 0 M λ × N λ 0 M λ × N λ [ Φ n 1 s ,m ] ],
Corr( g block-int ( i ) , g block-int ( j ) )= cov( g block-int ( i ) , g block-int ( j ) ) cov( g block-int ( i ) )cov( g block-int ( j ) ) ,

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