Abstract

Liquid crystal tunable filters (LCTF) are extensively used in hyperspectral imaging systems to successively acquire different spectral components of scenes by adjusting the center wavelength of the filter. However, the spectral and spatial resolutions of the imager are limited by the bandwidth of LCTF, and the pitch dimension of the detector, respectively. This paper applies compressive sensing principles to improve both of the spatial and spectral resolutions of the LCTF-based hyperspectral imaging system. An accurate transmission model of the LCTF is used to represent its bandpass filtering effects on the spectra. In addition, a random coded aperture placed behind the LCTF is used to modulate the spectral images in the spatial domain. Then, the three-dimensional encoded spectral images are projected onto a two-dimensional detector. Benefiting from the spectral-dependent transmission property of the LCTF, information of the entire spectrum is collected by a few snapshots using different center wavelengths of the LCTF. Super-resolution hyperspectral images can be reconstructed from a small set of compressive measurements by solving a convex optimization problem. Simulations and experiments show that the proposed method can effectively improve the spectral and spatial resolutions of traditional LCTF-based spectral imager without changing the structures of the LCTF and detector. Finally, a multi-channel spectral coding method is proposed to further increase the compression capacity of the system.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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  1. H. Kurosaki, H. Koshiishi, T. Suzuki, and K. Tsuchiya, “Development of tunable imaging spectro-polarimeter for remote sensing,” Adv. Space Res. 32(11), 2141–2146 (2003).
    [Crossref]
  2. S. C. Gebhart, R. C. Thompson, and A. Mahadevan-Jansen, “Liquid-crystal tunable filter spectral imaging for brain tumor demarcation,” Appl. Opt. 46(10), 1896–1910 (2007).
    [Crossref] [PubMed]
  3. S. J. Woltman, G. D. Jay, and G. P. Crawford, “Liquid-crystal materials find a new order in biomedical applications,” Nat. Mater. 6(12), 929–938 (2007).
    [Crossref] [PubMed]
  4. J. Beeckman, K. Neyts, and P. J. M. Vanbrabant, “Liquid-crystal photonic applications,” Opt. Eng. 50(8), 081202 (2011).
    [Crossref]
  5. W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
    [Crossref]
  6. A. Mian and R. Hartley, “Hyperspectral video restoration using optical flow and sparse coding,” Opt. Express 20(10), 10658–10673 (2012).
    [Crossref] [PubMed]
  7. D. Jiang, Y. Liu, W. Cao, and Z. Guo, “A compact novel microwave liquid crystal tunable band-pass filter,” Optik 127(20), 8374–8378 (2016).
    [Crossref]
  8. M. A. López-Álvarez, J. Hernández-Andrés, and J. Romero, “Developing an optimum computer-designed multispectral system comprising a monochrome ccd camera and a liquid-crystal tunable filter,” Appl. Opt. 47(24), 4381–4390 (2008).
    [Crossref] [PubMed]
  9. G. Shabtay, E. Eidinger, Z. Zalevsky, D. Mendlovic, and E. Marom, “Tunable birefringent filters – optimal iterative design,” Opt. Express 10(26), 1534–1541 (2002).
    [Crossref] [PubMed]
  10. O. Aharon and I. Abdulhalim, “Liquid crystal lyot tunable filter with extended free spectral range,” Opt. Express 17(14), 11426–11433 (2009).
    [Crossref] [PubMed]
  11. M. Abuleil and I. Abdulhalim, “Narrowband multispectral liquid crystal tunable filter,” Opt. Lett. 41(9), 1957–1960 (2016).
    [Crossref] [PubMed]
  12. A. Jullien, R. Pascal, U. Bortolozzo, N. Forget, and S. Residori, “High-resolution hyperspectral imaging with cascaded liquid crystal cells,” Optica 4(4), 400–405 (2017).
    [Crossref]
  13. R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50(7), 072601 (2011).
    [Crossref]
  14. G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. Geosci. Remote Sens. 33(1), 26–34 (1995).
    [Crossref]
  15. F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer, “Low-complexity lossless compression of hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett. 12(2), 138–141 (2005).
    [Crossref]
  16. Q. Du and J. E. Fowler, “Hyperspectral image compression using jpeg2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4(2), 201–205 (2007).
    [Crossref]
  17. D. L. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006).
    [Crossref]
  18. E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Probl. 23, 969–985 (2007).
    [Crossref]
  19. E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008).
    [Crossref]
  20. 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]
  21. A. Wagadarikar, R. John, R. Willett, and D. Brady, “Single disperser design for coded aperture snapshot spectral imaging,” Appl. Opt. 47(10), B44–B51 (2008).
    [Crossref] [PubMed]
  22. H. Rueda, H. Arguello, and G. R. Arce, “DMD-based implementation of patterned optical filter arrays for compressive spectral imaging,” J. Opt. Soc. Am. A 32(1), 80–89 (2015).
    [Crossref]
  23. H. Rueda, H. Arguello, and G. R. Arce, “Compressive spectral testbed imaging system based on thin-film color-patterned filter arrays,” Appl. Opt. 55(33), 9584–9593 (2016).
    [Crossref] [PubMed]
  24. T. Sun and K. Kelly, “Compressive sensing hyperspectral imager,” in Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest, OSA Technical Digest (CD) (Optical Society of America, 2009), paper CTuA5.
  25. 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]
  26. 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]
  27. I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultraspectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6, 23524 (2016).
    [Crossref]
  28. Y. Oiknine, I. August, and A. Stern, “Along-track scanning using a liquid crystal compressive hyperspectral imager,” Opt. Express 24(8), 8446–8457 (2016).
    [Crossref] [PubMed]
  29. J. M. Bioucas-Dias and M. A. T. Figueiredo, “A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration,” IEEE Trans. Image Process. 16(12), 2992–3004 (2007).
    [Crossref] [PubMed]
  30. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems,” IEEE J. Sel. Topics Signal Process. 1(4), 586–597 (2007).
    [Crossref]
  31. S. J. Wright, R. D. Nowak, and M. A. T. Figueiredo, “Sparse reconstruction by separable approximation,” IEEE Trans. Signal Process. 57(7), 2479–2493 (2009).
    [Crossref]
  32. J. Tan, Y. Ma, H. Rueda, D. Baron, and G. R Arce, “Compressive hyperspectral imaging via approximate message passing,” IEEE J. Sel. Topics Signal Process. 10(2), 389–401 (2016).
    [Crossref]
  33. A. Gilbert and P. Indyk, “Sparse recovery using sparse matrices,” Proc. IEEE 98(6), 937–947 (2010).
    [Crossref]
  34. B. Bah and J. Tanner, “Vanishingly sparse matrices and expander graphs, with application to compressed sensing,” IEEE Trans. Inf. Theory 59(11), 7491–7508 (2013).
    [Crossref]
  35. M. F. Duarte and R. G. Baraniuk, “Kronecker compressive sensing,” IEEE Trans. Image Process. 21(2), 494–504 (2012).
    [Crossref]
  36. B. Arad and O. Ben-Shahar, “Sparse Recovery of Hyperspectral Signal from Natural RGB Images,” in Proceedings of Computer Vision - ECCV 2016: 14th European Conference, (Springer, 2016), pp. 19–34.
  37. Y. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University, 2012).
    [Crossref]

2017 (1)

2016 (6)

Y. Oiknine, I. August, and A. Stern, “Along-track scanning using a liquid crystal compressive hyperspectral imager,” Opt. Express 24(8), 8446–8457 (2016).
[Crossref] [PubMed]

M. Abuleil and I. Abdulhalim, “Narrowband multispectral liquid crystal tunable filter,” Opt. Lett. 41(9), 1957–1960 (2016).
[Crossref] [PubMed]

H. Rueda, H. Arguello, and G. R. Arce, “Compressive spectral testbed imaging system based on thin-film color-patterned filter arrays,” Appl. Opt. 55(33), 9584–9593 (2016).
[Crossref] [PubMed]

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultraspectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6, 23524 (2016).
[Crossref]

D. Jiang, Y. Liu, W. Cao, and Z. Guo, “A compact novel microwave liquid crystal tunable band-pass filter,” Optik 127(20), 8374–8378 (2016).
[Crossref]

J. Tan, Y. Ma, H. Rueda, D. Baron, and G. R Arce, “Compressive hyperspectral imaging via approximate message passing,” IEEE J. Sel. Topics Signal Process. 10(2), 389–401 (2016).
[Crossref]

2015 (1)

2014 (1)

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]

2013 (2)

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]

B. Bah and J. Tanner, “Vanishingly sparse matrices and expander graphs, with application to compressed sensing,” IEEE Trans. Inf. Theory 59(11), 7491–7508 (2013).
[Crossref]

2012 (3)

M. F. Duarte and R. G. Baraniuk, “Kronecker compressive sensing,” IEEE Trans. Image Process. 21(2), 494–504 (2012).
[Crossref]

W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
[Crossref]

A. Mian and R. Hartley, “Hyperspectral video restoration using optical flow and sparse coding,” Opt. Express 20(10), 10658–10673 (2012).
[Crossref] [PubMed]

2011 (2)

J. Beeckman, K. Neyts, and P. J. M. Vanbrabant, “Liquid-crystal photonic applications,” Opt. Eng. 50(8), 081202 (2011).
[Crossref]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50(7), 072601 (2011).
[Crossref]

2010 (1)

A. Gilbert and P. Indyk, “Sparse recovery using sparse matrices,” Proc. IEEE 98(6), 937–947 (2010).
[Crossref]

2009 (2)

O. Aharon and I. Abdulhalim, “Liquid crystal lyot tunable filter with extended free spectral range,” Opt. Express 17(14), 11426–11433 (2009).
[Crossref] [PubMed]

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

2008 (3)

2007 (7)

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

E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Probl. 23, 969–985 (2007).
[Crossref]

Q. Du and J. E. Fowler, “Hyperspectral image compression using jpeg2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4(2), 201–205 (2007).
[Crossref]

S. J. Woltman, G. D. Jay, and G. P. Crawford, “Liquid-crystal materials find a new order in biomedical applications,” Nat. Mater. 6(12), 929–938 (2007).
[Crossref] [PubMed]

S. C. Gebhart, R. C. Thompson, and A. Mahadevan-Jansen, “Liquid-crystal tunable filter spectral imaging for brain tumor demarcation,” Appl. Opt. 46(10), 1896–1910 (2007).
[Crossref] [PubMed]

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]

2006 (1)

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

2005 (1)

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer, “Low-complexity lossless compression of hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett. 12(2), 138–141 (2005).
[Crossref]

2003 (1)

H. Kurosaki, H. Koshiishi, T. Suzuki, and K. Tsuchiya, “Development of tunable imaging spectro-polarimeter for remote sensing,” Adv. Space Res. 32(11), 2141–2146 (2003).
[Crossref]

2002 (1)

1995 (1)

G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. Geosci. Remote Sens. 33(1), 26–34 (1995).
[Crossref]

Abdulhalim, I.

Abousleman, G. P.

G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. Geosci. Remote Sens. 33(1), 26–34 (1995).
[Crossref]

AbuLeil, M.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultraspectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6, 23524 (2016).
[Crossref]

M. Abuleil and I. Abdulhalim, “Narrowband multispectral liquid crystal tunable filter,” Opt. Lett. 41(9), 1957–1960 (2016).
[Crossref] [PubMed]

Aharon, O.

Arad, B.

B. Arad and O. Ben-Shahar, “Sparse Recovery of Hyperspectral Signal from Natural RGB Images,” in Proceedings of Computer Vision - ECCV 2016: 14th European Conference, (Springer, 2016), pp. 19–34.

Arce, G. R

J. Tan, Y. Ma, H. Rueda, D. Baron, and G. R Arce, “Compressive hyperspectral imaging via approximate message passing,” IEEE J. Sel. Topics Signal Process. 10(2), 389–401 (2016).
[Crossref]

Arce, G. R.

Arguello, H.

August, I.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultraspectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6, 23524 (2016).
[Crossref]

Y. Oiknine, I. August, and A. Stern, “Along-track scanning using a liquid crystal compressive hyperspectral imager,” Opt. Express 24(8), 8446–8457 (2016).
[Crossref] [PubMed]

August, Y.

Bah, B.

B. Bah and J. Tanner, “Vanishingly sparse matrices and expander graphs, with application to compressed sensing,” IEEE Trans. Inf. Theory 59(11), 7491–7508 (2013).
[Crossref]

Baraniuk, R. G.

M. F. Duarte and R. G. Baraniuk, “Kronecker compressive sensing,” IEEE Trans. Image Process. 21(2), 494–504 (2012).
[Crossref]

Baron, D.

J. Tan, Y. Ma, H. Rueda, D. Baron, and G. R Arce, “Compressive hyperspectral imaging via approximate message passing,” IEEE J. Sel. Topics Signal Process. 10(2), 389–401 (2016).
[Crossref]

Beeckman, J.

J. Beeckman, K. Neyts, and P. J. M. Vanbrabant, “Liquid-crystal photonic applications,” Opt. Eng. 50(8), 081202 (2011).
[Crossref]

Ben-Shahar, O.

B. Arad and O. Ben-Shahar, “Sparse Recovery of Hyperspectral Signal from Natural RGB Images,” in Proceedings of Computer Vision - ECCV 2016: 14th European Conference, (Springer, 2016), pp. 19–34.

Bioucas-Dias, J. M.

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

Bortolozzo, U.

Brady, D.

Brady, D. J.

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]

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]

Candes, E. J.

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

Candès, E.

E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Probl. 23, 969–985 (2007).
[Crossref]

Cao, W.

D. Jiang, Y. Liu, W. Cao, and Z. Guo, “A compact novel microwave liquid crystal tunable band-pass filter,” Optik 127(20), 8374–8378 (2016).
[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]

Carpentieri, B.

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer, “Low-complexity lossless compression of hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett. 12(2), 138–141 (2005).
[Crossref]

Crawford, G. P.

S. J. Woltman, G. D. Jay, and G. P. Crawford, “Liquid-crystal materials find a new order in biomedical applications,” Nat. Mater. 6(12), 929–938 (2007).
[Crossref] [PubMed]

Donoho, D. L.

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

Du, Q.

Q. Du and J. E. Fowler, “Hyperspectral image compression using jpeg2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4(2), 201–205 (2007).
[Crossref]

Duarte, M. F.

M. F. Duarte and R. G. Baraniuk, “Kronecker compressive sensing,” IEEE Trans. Image Process. 21(2), 494–504 (2012).
[Crossref]

Eidinger, E.

Eldar, Y.

Y. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University, 2012).
[Crossref]

Figueiredo, M. A. T.

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

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

Forget, N.

Fowler, J. E.

Q. Du and J. E. Fowler, “Hyperspectral image compression using jpeg2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4(2), 201–205 (2007).
[Crossref]

Gebhart, S. C.

Gehm, M. E.

Gilbert, A.

A. Gilbert and P. Indyk, “Sparse recovery using sparse matrices,” Proc. IEEE 98(6), 937–947 (2010).
[Crossref]

Gitaitis, R. D.

W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
[Crossref]

Guo, Z.

D. Jiang, Y. Liu, W. Cao, and Z. Guo, “A compact novel microwave liquid crystal tunable band-pass filter,” Optik 127(20), 8374–8378 (2016).
[Crossref]

Hartley, R.

Hernández-Andrés, J.

Hunt, B. R.

G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. Geosci. Remote Sens. 33(1), 26–34 (1995).
[Crossref]

Indyk, P.

A. Gilbert and P. Indyk, “Sparse recovery using sparse matrices,” Proc. IEEE 98(6), 937–947 (2010).
[Crossref]

Jay, G. D.

S. J. Woltman, G. D. Jay, and G. P. Crawford, “Liquid-crystal materials find a new order in biomedical applications,” Nat. Mater. 6(12), 929–938 (2007).
[Crossref] [PubMed]

Jiang, D.

D. Jiang, Y. Liu, W. Cao, and Z. Guo, “A compact novel microwave liquid crystal tunable band-pass filter,” Optik 127(20), 8374–8378 (2016).
[Crossref]

John, R.

Jullien, A.

Kelly, K.

T. Sun and K. Kelly, “Compressive sensing hyperspectral imager,” in Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest, OSA Technical Digest (CD) (Optical Society of America, 2009), paper CTuA5.

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]

Koshiishi, H.

H. Kurosaki, H. Koshiishi, T. Suzuki, and K. Tsuchiya, “Development of tunable imaging spectro-polarimeter for remote sensing,” Adv. Space Res. 32(11), 2141–2146 (2003).
[Crossref]

Kurosaki, H.

H. Kurosaki, H. Koshiishi, T. Suzuki, and K. Tsuchiya, “Development of tunable imaging spectro-polarimeter for remote sensing,” Adv. Space Res. 32(11), 2141–2146 (2003).
[Crossref]

Kutyniok, G.

Y. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University, 2012).
[Crossref]

Li, C.

W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
[Crossref]

Liu, Y.

D. Jiang, Y. Liu, W. Cao, and Z. Guo, “A compact novel microwave liquid crystal tunable band-pass filter,” Optik 127(20), 8374–8378 (2016).
[Crossref]

López-Álvarez, M. A.

Ma, Y.

J. Tan, Y. Ma, H. Rueda, D. Baron, and G. R Arce, “Compressive hyperspectral imaging via approximate message passing,” IEEE J. Sel. Topics Signal Process. 10(2), 389–401 (2016).
[Crossref]

Mahadevan-Jansen, A.

Marcellin, M. W.

G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. Geosci. Remote Sens. 33(1), 26–34 (1995).
[Crossref]

Marcia, R. F.

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50(7), 072601 (2011).
[Crossref]

Marom, E.

Mendlovic, D.

Mian, A.

Motta, G.

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer, “Low-complexity lossless compression of hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett. 12(2), 138–141 (2005).
[Crossref]

Neyts, K.

J. Beeckman, K. Neyts, and P. J. M. Vanbrabant, “Liquid-crystal photonic applications,” Opt. Eng. 50(8), 081202 (2011).
[Crossref]

Nichols, J. M.

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50(7), 072601 (2011).
[Crossref]

Nowak, R. D.

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

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

Oiknine, Y.

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultraspectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6, 23524 (2016).
[Crossref]

Y. Oiknine, I. August, and A. Stern, “Along-track scanning using a liquid crystal compressive hyperspectral imager,” Opt. Express 24(8), 8446–8457 (2016).
[Crossref] [PubMed]

Pascal, R.

Rains, G. C.

W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
[Crossref]

Residori, S.

Rivenson, Y.

Rizzo, F.

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer, “Low-complexity lossless compression of hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett. 12(2), 138–141 (2005).
[Crossref]

Romberg, J.

E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Probl. 23, 969–985 (2007).
[Crossref]

Romero, J.

Rueda, H.

Schulz, T. J.

Shabtay, G.

Stern, A.

Storer, J. A.

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer, “Low-complexity lossless compression of hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett. 12(2), 138–141 (2005).
[Crossref]

Sun, T.

T. Sun and K. Kelly, “Compressive sensing hyperspectral imager,” in Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest, OSA Technical Digest (CD) (Optical Society of America, 2009), paper CTuA5.

Suzuki, T.

H. Kurosaki, H. Koshiishi, T. Suzuki, and K. Tsuchiya, “Development of tunable imaging spectro-polarimeter for remote sensing,” Adv. Space Res. 32(11), 2141–2146 (2003).
[Crossref]

Tan, J.

J. Tan, Y. Ma, H. Rueda, D. Baron, and G. R Arce, “Compressive hyperspectral imaging via approximate message passing,” IEEE J. Sel. Topics Signal Process. 10(2), 389–401 (2016).
[Crossref]

Tanner, J.

B. Bah and J. Tanner, “Vanishingly sparse matrices and expander graphs, with application to compressed sensing,” IEEE Trans. Inf. Theory 59(11), 7491–7508 (2013).
[Crossref]

Thompson, R. C.

Tollner, E. W.

W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
[Crossref]

Tsuchiya, K.

H. Kurosaki, H. Koshiishi, T. Suzuki, and K. Tsuchiya, “Development of tunable imaging spectro-polarimeter for remote sensing,” Adv. Space Res. 32(11), 2141–2146 (2003).
[Crossref]

Vachman, C.

Vanbrabant, P. J. M.

J. Beeckman, K. Neyts, and P. J. M. Vanbrabant, “Liquid-crystal photonic applications,” Opt. Eng. 50(8), 081202 (2011).
[Crossref]

Wagadarikar, A.

Wakin, M. B.

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

Wang, W.

W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
[Crossref]

Willett, R.

Willett, R. M.

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50(7), 072601 (2011).
[Crossref]

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]

Woltman, S. J.

S. J. Woltman, G. D. Jay, and G. P. Crawford, “Liquid-crystal materials find a new order in biomedical applications,” Nat. Mater. 6(12), 929–938 (2007).
[Crossref] [PubMed]

Wright, S. J.

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

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

Zalevsky, Z.

Adv. Space Res. (1)

H. Kurosaki, H. Koshiishi, T. Suzuki, and K. Tsuchiya, “Development of tunable imaging spectro-polarimeter for remote sensing,” Adv. Space Res. 32(11), 2141–2146 (2003).
[Crossref]

Appl. Opt. (5)

Comput. Electron. Agr. (1)

W. Wang, C. Li, E. W. Tollner, G. C. Rains, and R. D. Gitaitis, “A liquid crystal tunable filter based shortwave infrared spectral imaging system: Calibration and characterization,” Comput. Electron. Agr. 80, 135–144 (2012).
[Crossref]

IEEE Geosci. Remote Sens. Lett. (1)

Q. Du and J. E. Fowler, “Hyperspectral image compression using jpeg2000 and principal component analysis,” IEEE Geosci. Remote Sens. Lett. 4(2), 201–205 (2007).
[Crossref]

IEEE J. Sel. Topics Signal Process. (2)

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

J. Tan, Y. Ma, H. Rueda, D. Baron, and G. R Arce, “Compressive hyperspectral imaging via approximate message passing,” IEEE J. Sel. Topics Signal Process. 10(2), 389–401 (2016).
[Crossref]

IEEE Signal Process. Lett. (1)

F. Rizzo, B. Carpentieri, G. Motta, and J. A. Storer, “Low-complexity lossless compression of hyperspectral imagery via linear prediction,” IEEE Signal Process. Lett. 12(2), 138–141 (2005).
[Crossref]

IEEE Signal Process. Mag. (2)

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]

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

IEEE Trans. Geosci. Remote Sens. (1)

G. P. Abousleman, M. W. Marcellin, and B. R. Hunt, “Compression of hyperspectral imagery using the 3-D DCT and hybrid DPCM/DCT,” IEEE Trans. Geosci. Remote Sens. 33(1), 26–34 (1995).
[Crossref]

IEEE Trans. Image Process. (2)

J. M. Bioucas-Dias and M. A. T. 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. F. Duarte and R. G. Baraniuk, “Kronecker compressive sensing,” IEEE Trans. Image Process. 21(2), 494–504 (2012).
[Crossref]

IEEE Trans. Inf. Theory (2)

B. Bah and J. Tanner, “Vanishingly sparse matrices and expander graphs, with application to compressed sensing,” IEEE Trans. Inf. Theory 59(11), 7491–7508 (2013).
[Crossref]

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

IEEE Trans. Signal Process. (1)

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

Inverse Probl. (1)

E. Candès and J. Romberg, “Sparsity and incoherence in compressive sampling,” Inverse Probl. 23, 969–985 (2007).
[Crossref]

J. Opt. Soc. Am. A (1)

Nat. Mater. (1)

S. J. Woltman, G. D. Jay, and G. P. Crawford, “Liquid-crystal materials find a new order in biomedical applications,” Nat. Mater. 6(12), 929–938 (2007).
[Crossref] [PubMed]

Opt. Eng. (2)

J. Beeckman, K. Neyts, and P. J. M. Vanbrabant, “Liquid-crystal photonic applications,” Opt. Eng. 50(8), 081202 (2011).
[Crossref]

R. M. Willett, R. F. Marcia, and J. M. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng. 50(7), 072601 (2011).
[Crossref]

Opt. Express (5)

Opt. Lett. (1)

Optica (1)

Optik (1)

D. Jiang, Y. Liu, W. Cao, and Z. Guo, “A compact novel microwave liquid crystal tunable band-pass filter,” Optik 127(20), 8374–8378 (2016).
[Crossref]

Proc. IEEE (1)

A. Gilbert and P. Indyk, “Sparse recovery using sparse matrices,” Proc. IEEE 98(6), 937–947 (2010).
[Crossref]

Sci. Rep. (1)

I. August, Y. Oiknine, M. AbuLeil, I. Abdulhalim, and A. Stern, “Miniature compressive ultraspectral imaging system utilizing a single liquid crystal phase retarder,” Sci. Rep. 6, 23524 (2016).
[Crossref]

Other (3)

T. Sun and K. Kelly, “Compressive sensing hyperspectral imager,” in Frontiers in Optics 2009/Laser Science XXV/Fall 2009 OSA Optics & Photonics Technical Digest, OSA Technical Digest (CD) (Optical Society of America, 2009), paper CTuA5.

B. Arad and O. Ben-Shahar, “Sparse Recovery of Hyperspectral Signal from Natural RGB Images,” in Proceedings of Computer Vision - ECCV 2016: 14th European Conference, (Springer, 2016), pp. 19–34.

Y. Eldar and G. Kutyniok, Compressed Sensing: Theory and Applications (Cambridge University, 2012).
[Crossref]

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Figures (13)

Fig. 1
Fig. 1 (a) Traditional LCTF-based spectral imaging system, and (b) the sketch of the proposed CATF spectral imaging system.
Fig. 2
Fig. 2 The ideal and actual transmission functions of LCTFs.
Fig. 3
Fig. 3 The spectral filtering characteristics of the LCTF.
Fig. 4
Fig. 4 The spatial coding and compressive measurements of the spectral images.
Fig. 5
Fig. 5 An example of the transmission matrix for a hyperspectral data cube with Nx = Ny = 4, Nλ = 16, K = 5, L = 4 and Mx = My = 1.
Fig. 6
Fig. 6 The LCTF transmission functions used in the simulations. The center wavelengths of LCTF are ranged from 500nm to 710nm with the interval of 10nm.
Fig. 7
Fig. 7 (Top) the original spectral images, (middle) the reconstructed spectral images using the CATF system, and (bottom) the reconstructed spectral images using the traditional system.
Fig. 8
Fig. 8 The original and reconstructed spectra of three representative pixels on the scene using different methods. (a) The RGB image of the scene, and the spectra on the pixels (b) P1, (c) P2 and (d) P3.
Fig. 9
Fig. 9 Testbed of the proposed CATF spectral imager.
Fig. 10
Fig. 10 The actual spectral transmission functions of the entire system corresponding to different center wavelengths.
Fig. 11
Fig. 11 The reconstructed spectral images using (top) the CATF system and (bottom) the traditional system.
Fig. 12
Fig. 12 The spectra of three representative pixels on the scene obtained by the testbed. (a) The RGB image of the scene, and the spectra on the pixels of (b) P1, (c) P2 and (d) P3.
Fig. 13
Fig. 13 An example of the transmission matrix using multi-channel spectral coding method.

Tables (1)

Tables Icon

Table 1 The maximum, minimum and average PSNRs of the reconstructed spectral images for different number of Q.

Equations (19)

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f = Ψ θ ,
g = Φ f = Φ Ψ θ ,
M C μ 2 ( Φ , Ψ ) S log N ,
μ ( Φ , Ψ ) = N max m , n | φ m , ψ n | ,
θ ^ = arg m i n θ θ 1 subject to g A θ 2 ε ,
T s ( λ ) = [ sin ( 2 n π d Δ n / λ ) 2 n sin ( π d Δ n / λ ) ] 2 ,
f 1 l ( x , y , λ ) = f 0 ( x , y , λ ) T s l ( λ ) .
f 2 l , k ( x , y , λ ) = f 1 l ( x , y , λ ) T c k ( x , y ) = f 0 ( x , y , λ ) T s l ( λ ) T c l ( x , y ) ,
g l , k ( x , y ) = f 2 l , k ( x , y , λ ) d λ = f 0 ( x , y , λ ) T s l ( λ ) T c k ( x , y ) d λ .
T l , k ( x , y , λ ) = T s l ( λ ) T c k ( x , y ) = n x , n y T n x , n y , n λ l , k rect ( x δ c n x , y δ c n y ) ,
G m x , m y l , k = g l , k ( x , y ) rect ( x δ d m x , y δ d m y ) d x f y = n x , n y T n x , n y , n λ l , k rect ( x δ c n x , y δ c n y ) rect( x δ d m x , y δ d m y ) f 0 ( x , y , λ ) d x d y d λ = n x = R ( m x 1 ) + 1 m x R n y = R ( m y 1 ) + 1 m y R n λ = 1 N λ F n x , n y , n λ T n x , n y , n λ l , k ,
g = Φ f ,
Φ = Φ x y Φ λ ,
Φ x y = [ Φ x y 1 0 K × R 2 0 K × R 2 0 K × R 2 Φ x y 2 0 K × R 2 0 K × R 2 0 K × R 2 Φ x y M x × M y ] ,
γ = N x N y N λ K L M x M y = R 2 N λ K L .
f = Ψ θ ,
g = Φ Ψ θ + ω = A θ + ω ,
θ ^ = arg min θ θ 1 subject to g A θ 2 ε ,
T P q ( λ ) = i = 1 P q [ sin ( 2 n π d Δ n i / λ ) 2 n sin ( π d Δ n i / λ ) ] 2 ,

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