Abstract

In this paper hyperspectral imaging in the mid-infrared wavelength region is realised using nonlinear frequency upconversion. The infrared light is converted to the near-infrared region for detection with a Si-based CCD camera. The object is translated in a predefined grid by motorized actuators and an image is recorded for each position. A sequence of such images is post-processed into a series of monochromatic images in a wavelength range defined by the phasematch condition and numerical aperture of the upconversion system. A standard USAF resolution target and a polystyrene film are used to impart spatial and spectral information unto the source.

© 2015 Optical Society of America

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References

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  1. A. Rogalski, “History of infrared detectors,” Opto-Electron. Rev. 20(3), 279–308 (2012).
    [Crossref]
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    [Crossref]
  3. D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
    [Crossref]
  4. R. K. Reddy, M. J. Walsh, M. V. Schulmerich, P. S. Carney, and R. Bhargava, “High-definition infrared spectroscopic imaging,” Appl. Spectrosc. 67(1), 93–105 (2013).
    [Crossref] [PubMed]
  5. B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
    [Crossref] [PubMed]
  6. P. R. Griffiths and J. A. de Haseth, Fourier Transform Infrared Spectrometry, 2nd ed. (Wiley, 2007).
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    [Crossref]
  8. J. S. Dam, C. Pedersen, and P. Tidemand-Lichtenberg, “Theory for upconversion of incoherent images,” Opt. Express 20(2), 1475–1482 (2012).
    [Crossref] [PubMed]
  9. J. S. Dam, C. Pedersen, and P. Tidemand-Lichtenberg, “High-resolution two-dimensional image upconversion of incoherent light,” Opt. Lett. 35(22), 3796–3798 (2010).
    [Crossref] [PubMed]
  10. L. M. Kehlet, P. Tidemand-Lichtenberg, J. S. Dam, and C. Pedersen, “Infrared upconversion hyperspectral imaging,” Opt. Lett. 40(6), 938–941 (2015).
    [Crossref] [PubMed]
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    [Crossref]
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    [Crossref]
  14. J. H. Halton, “On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals,” Numer. Math. 2(1), 84–90 (1960).
    [Crossref]

2015 (2)

2014 (1)

N. Sanders, J. S. Dam, P. Tidemand-Lichtenberg, and C. Pedersen, “Near diffraction limited mid-IR spectromicroscopy, using frequency upconversion,” Proc. SPIE 8964, 89641L (2014).
[Crossref]

2013 (2)

R. K. Reddy, M. J. Walsh, M. V. Schulmerich, P. S. Carney, and R. Bhargava, “High-definition infrared spectroscopic imaging,” Appl. Spectrosc. 67(1), 93–105 (2013).
[Crossref] [PubMed]

Q. Zhou, K. Huang, H. Pan, E. Wu, and H. Zeng, “Ultrasensitive mid-infrared up-conversion imaging at few-photon level,” Appl. Phys. Lett. 102(24), 241110 (2013).
[Crossref]

2012 (4)

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

A. Rogalski, “History of infrared detectors,” Opto-Electron. Rev. 20(3), 279–308 (2012).
[Crossref]

J. S. Dam, P. Tidemand-Lichtenberg, and C. Pedersen, “Room-temperature mid-infrared single-photon spectral imaging,” Nat. Photonics 6(11), 788–793 (2012).
[Crossref]

J. S. Dam, C. Pedersen, and P. Tidemand-Lichtenberg, “Theory for upconversion of incoherent images,” Opt. Express 20(2), 1475–1482 (2012).
[Crossref] [PubMed]

2010 (1)

2007 (1)

A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias, “Hyperspectral imaging – an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18(12), 590–598 (2007).
[Crossref]

2005 (1)

B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
[Crossref] [PubMed]

1960 (1)

J. H. Halton, “On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals,” Numer. Math. 2(1), 84–90 (1960).
[Crossref]

Aleixos, N.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

Bhargava, R.

Blasco, J.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

Cao, Y.

B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
[Crossref] [PubMed]

Capmany, J.

Carney, P. S.

Cubero, S.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

Cullen, P. J.

A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias, “Hyperspectral imaging – an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18(12), 590–598 (2007).
[Crossref]

Dam, J. S.

Dewhirst, M. W.

B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
[Crossref] [PubMed]

Donovan, O.

B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
[Crossref] [PubMed]

Downey, G.

A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias, “Hyperspectral imaging – an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18(12), 590–598 (2007).
[Crossref]

Frias, J. M.

A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias, “Hyperspectral imaging – an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18(12), 590–598 (2007).
[Crossref]

García-Navarrete, O. L.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

Gómez-Sanchis, J.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

Gowen, A. A.

A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias, “Hyperspectral imaging – an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18(12), 590–598 (2007).
[Crossref]

Halton, J. H.

J. H. Halton, “On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals,” Numer. Math. 2(1), 84–90 (1960).
[Crossref]

Huang, K.

Q. Zhou, K. Huang, H. Pan, E. Wu, and H. Zeng, “Ultrasensitive mid-infrared up-conversion imaging at few-photon level,” Appl. Phys. Lett. 102(24), 241110 (2013).
[Crossref]

Kehlet, L. M.

Lorente, D.

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

Maestre, H.

Moeller, B. J.

B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
[Crossref] [PubMed]

O’Donnell, C. P.

A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias, “Hyperspectral imaging – an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18(12), 590–598 (2007).
[Crossref]

Pan, H.

Q. Zhou, K. Huang, H. Pan, E. Wu, and H. Zeng, “Ultrasensitive mid-infrared up-conversion imaging at few-photon level,” Appl. Phys. Lett. 102(24), 241110 (2013).
[Crossref]

Pedersen, C.

Reddy, R. K.

Rogalski, A.

A. Rogalski, “History of infrared detectors,” Opto-Electron. Rev. 20(3), 279–308 (2012).
[Crossref]

Sanders, N.

N. Sanders, J. S. Dam, P. Tidemand-Lichtenberg, and C. Pedersen, “Near diffraction limited mid-IR spectromicroscopy, using frequency upconversion,” Proc. SPIE 8964, 89641L (2014).
[Crossref]

Schulmerich, M. V.

Sorg, B. S.

B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
[Crossref] [PubMed]

Tidemand-Lichtenberg, P.

Torregrosa, A. J.

Walsh, M. J.

Wu, E.

Q. Zhou, K. Huang, H. Pan, E. Wu, and H. Zeng, “Ultrasensitive mid-infrared up-conversion imaging at few-photon level,” Appl. Phys. Lett. 102(24), 241110 (2013).
[Crossref]

Zeng, H.

Q. Zhou, K. Huang, H. Pan, E. Wu, and H. Zeng, “Ultrasensitive mid-infrared up-conversion imaging at few-photon level,” Appl. Phys. Lett. 102(24), 241110 (2013).
[Crossref]

Zhou, Q.

Q. Zhou, K. Huang, H. Pan, E. Wu, and H. Zeng, “Ultrasensitive mid-infrared up-conversion imaging at few-photon level,” Appl. Phys. Lett. 102(24), 241110 (2013).
[Crossref]

Appl. Phys. Lett. (1)

Q. Zhou, K. Huang, H. Pan, E. Wu, and H. Zeng, “Ultrasensitive mid-infrared up-conversion imaging at few-photon level,” Appl. Phys. Lett. 102(24), 241110 (2013).
[Crossref]

Appl. Spectrosc. (1)

Food Bioprocess Technol. (1)

D. Lorente, N. Aleixos, J. Gómez-Sanchis, S. Cubero, O. L. García-Navarrete, and J. Blasco, “Recent advances and applications of hyperspectral Imaging for fruit and vegetable quality assessment,” Food Bioprocess Technol. 5(4), 1121–1142 (2012).
[Crossref]

J. Biomed. Opt. (1)

B. S. Sorg, B. J. Moeller, O. Donovan, Y. Cao, and M. W. Dewhirst, “Hyperspectral imaging of hemoglobin saturation in tumor microvasculature and tumor hypoxia development,” J. Biomed. Opt. 10(4), 044004 (2005).
[Crossref] [PubMed]

Nat. Photonics (1)

J. S. Dam, P. Tidemand-Lichtenberg, and C. Pedersen, “Room-temperature mid-infrared single-photon spectral imaging,” Nat. Photonics 6(11), 788–793 (2012).
[Crossref]

Numer. Math. (1)

J. H. Halton, “On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals,” Numer. Math. 2(1), 84–90 (1960).
[Crossref]

Opt. Express (1)

Opt. Lett. (3)

Opto-Electron. Rev. (1)

A. Rogalski, “History of infrared detectors,” Opto-Electron. Rev. 20(3), 279–308 (2012).
[Crossref]

Proc. SPIE (1)

N. Sanders, J. S. Dam, P. Tidemand-Lichtenberg, and C. Pedersen, “Near diffraction limited mid-IR spectromicroscopy, using frequency upconversion,” Proc. SPIE 8964, 89641L (2014).
[Crossref]

Trends Food Sci. Technol. (1)

A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G. Downey, and J. M. Frias, “Hyperspectral imaging – an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18(12), 590–598 (2007).
[Crossref]

Other (1)

P. R. Griffiths and J. A. de Haseth, Fourier Transform Infrared Spectrometry, 2nd ed. (Wiley, 2007).

Supplementary Material (4)

NameDescription
» Visualization 1: MP4 (185 KB)      Video showing some of a measurement series using a quasi-random Halton grid.
» Visualization 2: MP4 (146 KB)      Video showing some of a measurement series using a regular square grid.
» Visualization 3: MP4 (205 KB)      Monochromatic images reconstructed from a measurement using a quasi-random Halton grid.
» Visualization 4: MP4 (144 KB)      Monochromatic images reconstructed from a measurement using a regular square grid.

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Figures (6)

Fig. 1
Fig. 1 Schematic showing the basis of this method of creating monochromatic images. Top shows three different positions of the FoV relative to the object. Bottom shows the three corresponding images where the circular areas represent the FoV of the upconverter and the color gradient the wavelength distribution within it. Note that it is the object that is moved between the captured frames, in the experiments this is done with motorized actuators. Monochromatic images can be created by combining parts from different captured frames with the desired wavelength.
Fig. 2
Fig. 2 Schematic of the experimental setup used for this paper. The IR light is provided by a hot filament, it then passes through the polystyrene film and the resolution target before entering the upconversion module. The resulting NIR light is then captured by a CCD camera.
Fig. 3
Fig. 3 Schematic showing the post processing turning the recorded images (top) into monochromatic images (bottom right). Here only three images are shown while, in our case, an actual measurement consists of 256 frames. Using the motor positions at the acquisition time the recorded images are translated such that the spatial features are overlaid (bottom left). The calculated wavelength distribution is then used in order to generate spectra at each pixel position. These are then sampled to create the monochromatic images.
Fig. 4
Fig. 4 Examples of measured images without resolution target. Note the characteristic ring pattern associated with the spectrum of polystyrene. The irregularities and spectral information of the source is largely removed in the ratioed image.
Fig. 5
Fig. 5 Example of a recorded image of the USAF resolution target group 0, in which the largest lines are 500 µm. A measurement consists of 256 such images in our case. Note that the pattern of the resolution target is superimposed on the concentric circles of the polystyrene spectrum. Movies showing a section of the measurements can be seen in Visualization 1 and Visualization 2 for the quasi-random and regular grid respectively. This image has slightly enhanced contrast to be more easily viewable.
Fig. 6
Fig. 6 Examples of monochromatic images obtained. (Left) shows an image created from frames acquired using a quasi-random grid based on the Halton sequence. (Right) is based on a measurement using a regular square grid. Movies of the series can be found in Visualization 3 and Visualization 4 for the quasi-random and regular grid respectively. The bandwidth of these monochromatic images is on the order of 10 nm in the beginning of the series. Here the spectral range covered by the reconstructed images is 3.24-3.41 µm. In order to properly observe the differences between the sampling strategies, it is suggested to view the accompanying visualization.

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