Abstract

We introduce a compressive sensing based approach for single pixel hyperspectral chemical imaging in a broad spectral range in the near-infrared. Fully integrated MEMS based Fabry-Pérot tunable filter spectrometers and a digital micro-mirror device were employed to achieve spectral and spatial resolution, respectively. The available spectral range from 1500 to 2200 nm covers molecular overtone vibrations enabling chemical identification. Hyperspectral images of different adhesives deposited on a textile were recorded revealing their chemical composition. Furthermore, spectrally resolved near-infrared images with compression rates up to 90% are presented. The approach of single pixel imaging illustrates a promising technology for the infrared spectral range superior to conventionally used costly focal plane arrays.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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    [Crossref]

2019 (1)

2018 (2)

J. Kilgus, G. Langer, K. Duswald, R. Zimmerleiter, I. Zorin, T. Berer, and M. Brandstetter, “Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser,” Opt. Express 26(23), 30644–30654 (2018).
[Crossref] [PubMed]

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

2017 (3)

K. Shibuya, T. Minamikawa, Y. Mizutani, H. Yamamoto, K. Minoshima, T. Yasui, and T. Iwata, “Scan-less hyperspectral dual-comb single-pixel-imaging in both amplitude and phase,” Opt. Express 25(18), 21947–21957 (2017).
[Crossref] [PubMed]

M.-J. Sun, L.-T. Meng, M. P. Edgar, M. J. Padgett, and N. Radwell, “A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging,” Sci. Rep. 7(1), 3464 (2017).
[Crossref] [PubMed]

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

2015 (2)

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[Crossref]

2014 (2)

N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1(5), 285 (2014).
[Crossref]

M. Manley, “Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials,” Chem. Soc. Rev. 43(24), 8200–8214 (2014).
[Crossref] [PubMed]

2013 (4)

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]

S. S. Welsh, M. P. Edgar, R. Bowman, P. Jonathan, B. Sun, and M. J. Padgett, “Fast full-color computational imaging with single-pixel detectors,” Opt. Express 21(20), 23068–23074 (2013).
[Crossref] [PubMed]

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

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

2012 (1)

F. Magalhães, F. M. Araújo, M. Correia, M. Abolbashari, and F. Farahi, “High-resolution hyperspectral single-pixel imaging system based on compressive sensing,” Opt. Eng. 51(7), 071406 (2012).
[Crossref]

2011 (1)

R. Tibshirani, “Regression shrinkage and selection via the lasso: a retrospective,” J. R. Stat. Soc. Ser. B. Stat. Methodol. 73(3), 273–282 (2011).
[Crossref]

2010 (1)

C. R. Berger, Z. Wang, J. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Commun. Mag. 48(11), 164–174 (2010).
[Crossref]

2008 (1)

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

2006 (2)

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

E. J. Candes, 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]

1969 (1)

W. K. Pratt, J. Kane, and H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57(1), 58–68 (1969).
[Crossref]

Abolbashari, M.

F. Magalhães, F. M. Araújo, M. Correia, M. Abolbashari, and F. Farahi, “High-resolution hyperspectral single-pixel imaging system based on compressive sensing,” Opt. Eng. 51(7), 071406 (2012).
[Crossref]

Andersson, S. B.

S. B. Andersson and L. Y. Pao, “Non-raster sampling in atomic force microscopy: A compressed sensing approach,” in 2012 American Control Conference (ACC) (IEEE, 2012), pp. 2485–2490.
[Crossref]

Andrews, H. C.

W. K. Pratt, J. Kane, and H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57(1), 58–68 (1969).
[Crossref]

Araújo, F. M.

F. Magalhães, F. M. Araújo, M. Correia, M. Abolbashari, and F. Farahi, “High-resolution hyperspectral single-pixel imaging system based on compressive sensing,” Opt. Eng. 51(7), 071406 (2012).
[Crossref]

August, Y.

Baraniuk, R. G.

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

Berer, T.

Berger, C. R.

C. R. Berger, Z. Wang, J. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Commun. Mag. 48(11), 164–174 (2010).
[Crossref]

Bian, L.

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[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]

Bowman, R.

Bowman, R. W.

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

Brandstetter, M.

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

J. Kilgus, G. Langer, K. Duswald, R. Zimmerleiter, I. Zorin, T. Berer, and M. Brandstetter, “Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser,” Opt. Express 26(23), 30644–30654 (2018).
[Crossref] [PubMed]

Candes, E. J.

E. J. Candes, 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]

Correia, M.

F. Magalhães, F. M. Araújo, M. Correia, M. Abolbashari, and F. Farahi, “High-resolution hyperspectral single-pixel imaging system based on compressive sensing,” Opt. Eng. 51(7), 071406 (2012).
[Crossref]

Curk, T.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Dai, Q.

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[Crossref]

Davenport, M. A.

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

Demšar, J.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Denk, O.

Donoho, D. L.

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

Duarte, M. F.

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

Duswald, K.

J. Kilgus, G. Langer, K. Duswald, R. Zimmerleiter, I. Zorin, T. Berer, and M. Brandstetter, “Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser,” Opt. Express 26(23), 30644–30654 (2018).
[Crossref] [PubMed]

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

Edgar, M. P.

M.-J. Sun, L.-T. Meng, M. P. Edgar, M. J. Padgett, and N. Radwell, “A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging,” Sci. Rep. 7(1), 3464 (2017).
[Crossref] [PubMed]

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1(5), 285 (2014).
[Crossref]

S. S. Welsh, M. P. Edgar, R. Bowman, P. Jonathan, B. Sun, and M. J. Padgett, “Fast full-color computational imaging with single-pixel detectors,” Opt. Express 21(20), 23068–23074 (2013).
[Crossref] [PubMed]

Erjavec, A.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Farahi, F.

F. Magalhães, F. M. Araújo, M. Correia, M. Abolbashari, and F. Farahi, “High-resolution hyperspectral single-pixel imaging system based on compressive sensing,” Opt. Eng. 51(7), 071406 (2012).
[Crossref]

Gibson, G. M.

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1(5), 285 (2014).
[Crossref]

Gorup, C.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Hinterleitner, F.

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

Hocevar, T.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Huang, J.

C. R. Berger, Z. Wang, J. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Commun. Mag. 48(11), 164–174 (2010).
[Crossref]

Huang, K.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Hui, W.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

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]

Iwata, T.

Jin, S.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Jonathan, P.

Kane, J.

W. K. Pratt, J. Kane, and H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57(1), 58–68 (1969).
[Crossref]

Kelly, K. F.

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

Kilgus, J.

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

J. Kilgus, G. Langer, K. Duswald, R. Zimmerleiter, I. Zorin, T. Berer, and M. Brandstetter, “Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser,” Opt. Express 26(23), 30644–30654 (2018).
[Crossref] [PubMed]

Langer, G.

J. Kilgus, G. Langer, K. Duswald, R. Zimmerleiter, I. Zorin, T. Berer, and M. Brandstetter, “Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser,” Opt. Express 26(23), 30644–30654 (2018).
[Crossref] [PubMed]

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

Laska, J. N.

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

Liu, D.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Magalhães, F.

F. Magalhães, F. M. Araújo, M. Correia, M. Abolbashari, and F. Farahi, “High-resolution hyperspectral single-pixel imaging system based on compressive sensing,” Opt. Eng. 51(7), 071406 (2012).
[Crossref]

Manley, M.

M. Manley, “Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials,” Chem. Soc. Rev. 43(24), 8200–8214 (2014).
[Crossref] [PubMed]

Meng, L.-T.

M.-J. Sun, L.-T. Meng, M. P. Edgar, M. J. Padgett, and N. Radwell, “A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging,” Sci. Rep. 7(1), 3464 (2017).
[Crossref] [PubMed]

Milutinovic, M.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Minamikawa, T.

Minoshima, K.

Mitchell, K. J.

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1(5), 285 (2014).
[Crossref]

Mizutani, Y.

Možina, M.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Musiienko, A.

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]

Padgett, M. J.

M.-J. Sun, L.-T. Meng, M. P. Edgar, M. J. Padgett, and N. Radwell, “A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging,” Sci. Rep. 7(1), 3464 (2017).
[Crossref] [PubMed]

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1(5), 285 (2014).
[Crossref]

S. S. Welsh, M. P. Edgar, R. Bowman, P. Jonathan, B. Sun, and M. J. Padgett, “Fast full-color computational imaging with single-pixel detectors,” Opt. Express 21(20), 23068–23074 (2013).
[Crossref] [PubMed]

Pao, L. Y.

S. B. Andersson and L. Y. Pao, “Non-raster sampling in atomic force microscopy: A compressed sensing approach,” in 2012 American Control Conference (ACC) (IEEE, 2012), pp. 2485–2490.
[Crossref]

Polajnar, M.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Pratt, W. K.

W. K. Pratt, J. Kane, and H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57(1), 58–68 (1969).
[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]

Radwell, N.

M.-J. Sun, L.-T. Meng, M. P. Edgar, M. J. Padgett, and N. Radwell, “A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging,” Sci. Rep. 7(1), 3464 (2017).
[Crossref] [PubMed]

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

N. Radwell, K. J. Mitchell, G. M. Gibson, M. P. Edgar, R. Bowman, and M. J. Padgett, “Single-pixel infrared and visible microscope,” Optica 1(5), 285 (2014).
[Crossref]

Romberg, J.

E. J. Candes, 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]

Shi, Q.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Shibuya, K.

Štajdohar, M.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Staric, A.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Stern, A.

Sun, B.

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

S. S. Welsh, M. P. Edgar, R. Bowman, P. Jonathan, B. Sun, and M. J. Padgett, “Fast full-color computational imaging with single-pixel detectors,” Opt. Express 21(20), 23068–23074 (2013).
[Crossref] [PubMed]

Sun, M.-J.

M.-J. Sun, L.-T. Meng, M. P. Edgar, M. J. Padgett, and N. Radwell, “A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging,” Sci. Rep. 7(1), 3464 (2017).
[Crossref] [PubMed]

Sun, T.

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

Suo, J.

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[Crossref]

Takhar, D.

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

Tao, T.

E. J. Candes, 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]

Tian, J.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Tibshirani, R.

R. Tibshirani, “Regression shrinkage and selection via the lasso: a retrospective,” J. R. Stat. Soc. Ser. B. Stat. Methodol. 73(3), 273–282 (2011).
[Crossref]

Toplak, M.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Umek, L.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Wang, Y.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[Crossref]

Wang, Z.

C. R. Berger, Z. Wang, J. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Commun. Mag. 48(11), 164–174 (2010).
[Crossref]

Welsh, S. S.

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

S. S. Welsh, M. P. Edgar, R. Bowman, P. Jonathan, B. Sun, and M. J. Padgett, “Fast full-color computational imaging with single-pixel detectors,” Opt. Express 21(20), 23068–23074 (2013).
[Crossref] [PubMed]

Xiao, Y.

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[Crossref]

Yamamoto, H.

Yasui, T.

Ye, Q.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Ying, C.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Žagar, L.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Žbontar, J.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Zhang, L.

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[Crossref]

Zhou, S.

C. R. Berger, Z. Wang, J. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Commun. Mag. 48(11), 164–174 (2010).
[Crossref]

Zhou, W.

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

Žídek, K.

Zimmerleiter, R.

J. Kilgus, G. Langer, K. Duswald, R. Zimmerleiter, I. Zorin, T. Berer, and M. Brandstetter, “Diffraction limited mid-infrared reflectance microspectroscopy with a supercontinuum laser,” Opt. Express 26(23), 30644–30654 (2018).
[Crossref] [PubMed]

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

Žitnik, M.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Zorin, I.

Zupan, B.

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

Chem. Soc. Rev. (1)

M. Manley, “Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials,” Chem. Soc. Rev. 43(24), 8200–8214 (2014).
[Crossref] [PubMed]

IEEE Commun. Mag. (1)

C. R. Berger, Z. Wang, J. Huang, and S. Zhou, “Application of compressive sensing to sparse channel estimation,” IEEE Commun. Mag. 48(11), 164–174 (2010).
[Crossref]

IEEE Signal Process. Mag. (1)

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

IEEE Trans. Inf. Theory (2)

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

E. J. Candes, 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]

J. Commun. Netw. (Seoul) (1)

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]

J. Mach. Learn. Res. (1)

J. Demšar, T. Curk, A. Erjavec, Č. Gorup, T. Hočevar, M. Milutinovič, M. Možina, M. Polajnar, M. Toplak, A. Starič, M. Štajdohar, L. Umek, L. Žagar, J. Žbontar, M. Žitnik, and B. Zupan, “Orange: Data Mining Toolbox in Python,” J. Mach. Learn. Res. 14, 2349–2353 (2013).

J. R. Stat. Soc. Ser. B. Stat. Methodol. (1)

R. Tibshirani, “Regression shrinkage and selection via the lasso: a retrospective,” J. R. Stat. Soc. Ser. B. Stat. Methodol. 73(3), 273–282 (2011).
[Crossref]

Opt. Eng. (1)

F. Magalhães, F. M. Araújo, M. Correia, M. Abolbashari, and F. Farahi, “High-resolution hyperspectral single-pixel imaging system based on compressive sensing,” Opt. Eng. 51(7), 071406 (2012).
[Crossref]

Opt. Express (4)

Opt. Laser Technol. (1)

J. Suo, L. Bian, Y. Xiao, Y. Wang, L. Zhang, and Q. Dai, “A self-synchronized high speed computational ghost imaging system: A leap towards dynamic capturing,” Opt. Laser Technol. 74, 65–71 (2015).
[Crossref]

Opt. Lett. (1)

Optica (1)

Proc. IEEE (1)

W. K. Pratt, J. Kane, and H. C. Andrews, “Hadamard transform image coding,” Proc. IEEE 57(1), 58–68 (1969).
[Crossref]

Proceedings (1)

J. Kilgus, R. Zimmerleiter, K. Duswald, F. Hinterleitner, G. Langer, and M. Brandstetter, “Application of a Novel Low-Cost Hyperspectral Imaging Setup Operating in the Mid-Infrared Region,” Proceedings 2(13), 800 (2018).
[Crossref]

Sci. Rep. (3)

M.-J. Sun, L.-T. Meng, M. P. Edgar, M. J. Padgett, and N. Radwell, “A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging,” Sci. Rep. 7(1), 3464 (2017).
[Crossref] [PubMed]

S. Jin, W. Hui, Y. Wang, K. Huang, Q. Shi, C. Ying, D. Liu, Q. Ye, W. Zhou, and J. Tian, “Hyperspectral imaging using the single-pixel Fourier transform technique,” Sci. Rep. 7(1), 45209 (2017).
[Crossref] [PubMed]

M. P. Edgar, G. M. Gibson, R. W. Bowman, B. Sun, N. Radwell, K. J. Mitchell, S. S. Welsh, and M. J. Padgett, “Simultaneous real-time visible and infrared video with single-pixel detectors,” Sci. Rep. 5(1), 10669 (2015).
[Crossref] [PubMed]

Other (5)

G. Zhang, S. Jiao, and X. Xu, “Compressed sensing and reconstruction with Semi-Hadamard matrices,” in 2010 2nd International Conference on Signal Processing Systems (IEEE, 2010), pp. V1–194–V1-197.
[Crossref]

S. B. Andersson and L. Y. Pao, “Non-raster sampling in atomic force microscopy: A compressed sensing approach,” in 2012 American Control Conference (ACC) (IEEE, 2012), pp. 2485–2490.
[Crossref]

M. Sun, M. P. Edgar, D. B. Phillips, G. M. Gibson, and M. J. Padgett, “Infrared single-pixel imaging utilising microscanning,” Arxiv (2015).

D. Takhar, J. N. Laska, M. B. Wakin, M. F. Duarte, D. Baron, S. Sarvotham, K. F. Kelly, and R. G. Baraniuk, “A new compressive imaging camera architecture using optical-domain compression,” in C. A. Bouman, E. L. Miller, and I. Pollak, eds. (International Society for Optics and Photonics, 2006), Vol. 6065, pp. 606509–606509–10.

P. R. Griffiths and J. A. De Haseth, Fourier Transform Infrared Spectrometry (Wiley-Interscience, 2007).

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Figures (4)

Fig. 1
Fig. 1 Schematic drawing of the single pixel camera. Broadband light from a halogen lamp gets diffusely scattered by the sample. An objective lens projects the sample image onto the DMD which spatially modulates the scene. An optical magnification system is attached to focus the NIR radiation onto the MEMS-based spectrometer consisting of a tunable Fabry-Pérot cavity and an InGaAs detector.
Fig. 2
Fig. 2 Contribution of the most significant Hadamard patterns to the performance of the reconstruction. Images of a black toner logo on white paper were recorded at 2190 nm wavelength and reconstructed using different amounts of patterns. It can be seen that the patterns with large Norm(S) values have significantly higher influence on the reconstruction quality. The FOV is 25 mm by 25 mm.
Fig. 3
Fig. 3 Two different markers applied on standard white office paper; The letter ‘R’ was overpainted with a chemically different marker illustrated in (a); Photograph of the sample (b); NIR image recorded at 1820 nm wavelength (c); The written letter ‘R’ can be clearly recognized.
Fig. 4
Fig. 4 Multivariate analysis and classification of reconstructed absorption images of different adhesives on a textile; (a) Representative absorbance spectra (stacked) of the four adhesive types; (b) Principle component analysis of selected pixels from each adhesive (ellipses indicate class-wise 95% confidence intervals); (c) Photograph of the sample; (d) Pixel-wise classification from a logistic regression model fitted to data shown in (b); (e) Absorption image at 1765 nm; (f) Absorption image at 1605 nm; SPI parameters: 64 by 64 pixels and 50% compression; Blue: Adhesive 1, Orange: Adhesive 2, Green: Adhesive 3, Red: Adhesive 4, Grey: Background.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

y=ϕx.
DR=20log10( Pmax Pnoise ).
PSNR=20log10( Pmax ( i=1 N ( P0,iPr,i ) 2 /N ) ).

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