Abstract

Every imaging system requires a geometric calibration to yield accurate optical measurements. Geometric calibration typically involves imaging of a known calibration object and finding the parameters of a camera model and a model of optical aberrations. Optical aberrations can vary significantly across the wide spectral ranges of hyperspectral imaging systems, which can lead to inaccurate geometric calibrations if conventional methods were used. We propose a method based on a B-spline transformation field to align the spectral images of the calibration object to the model image of the calibration object. The degree of spatial alignment between the ideal and the spectral images is measured by normalized cross correlation. Geometric calibration was performed on a hyperspectral imaging system based on an acousto-optic tunable filter designed for the near-infrared spectral range (1.01.7μm). The proposed method can accurately characterize wavelength dependent optical aberrations and produce transformations for efficient subpixel geometric calibration.

© 2010 Optical Society of America

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References

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  1. D. Bannon, “Hyperspectral imaging: cubes and slices,” Nat. Photonics 3, 627–629 (2009).
    [CrossRef]
  2. C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review,” J. Pharm. Biomed. Anal. 48, 533–553 (2008).
    [CrossRef] [PubMed]
  3. A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
    [CrossRef] [PubMed]
  4. A. Gowen, C. O’Donnell, P. Cullen, G. Downey, and J. Frias, “Hyperspectral imaging—an emerging process analytical tool for food quality and safety control,” Trends Food Sci. Technol. 18, 590–598 (2007).
    [CrossRef]
  5. P. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytometry Part A 69, 835–841 (2006).
    [CrossRef]
  6. A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
    [CrossRef]
  7. V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003).
    [PubMed]
  8. J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).
    [CrossRef]
  9. D. C. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).
  10. J. Mallon and P. F. Whelan, “Calibration and removal of lateral chromatic aberration in images,” Pattern Recogn. Lett. 28, 125–135 (2007).
    [CrossRef]
  11. P. Brakhage, G. Notni, and R. Kowarschik, “Image aberrations in optical three-dimensional measurement systems with fringe projection,” Appl. Opt. 43, 3217–3223(2004).
    [CrossRef] [PubMed]
  12. C. Ricolfe-Viala and A. Sánchez-Salmerón, “Robust metric calibration of non-linear camera lens distortion,” Pattern Recogn. 43, 1688–1699 (2010).
    [CrossRef]
  13. Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
    [CrossRef]
  14. A. Machihin and V. Pozhar, “A spectral distortion correction method for an imaging spectrometer,” Instrum. Exp. Tech. 52, 847–853 (2009).
    [CrossRef]
  15. M. Unser, “Splines—a perfect fit for signal and image processing,” IEEE Signal Process Mag. 16, 22–38 (1999).
    [CrossRef]
  16. D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
    [CrossRef] [PubMed]
  17. F. L. Bookstein, “Principal warps: thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989).
    [CrossRef]
  18. C. T. Kelley, Iterative Methods for Optimization (Society for Industrial Mathematics, 1999).
    [CrossRef]
  19. J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010).
    [CrossRef]
  20. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the Fourth Alvey Vision Conference (University of Manchester, 1988), pp. 147–152.
  21. R. Willson, “Modeling and calibration of automated zoom lenses,” Ph.D. dissertation (Carnegie Mellon, 1994).

2010 (3)

C. Ricolfe-Viala and A. Sánchez-Salmerón, “Robust metric calibration of non-linear camera lens distortion,” Pattern Recogn. 43, 1688–1699 (2010).
[CrossRef]

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010).
[CrossRef]

2009 (2)

A. Machihin and V. Pozhar, “A spectral distortion correction method for an imaging spectrometer,” Instrum. Exp. Tech. 52, 847–853 (2009).
[CrossRef]

D. Bannon, “Hyperspectral imaging: cubes and slices,” Nat. Photonics 3, 627–629 (2009).
[CrossRef]

2008 (2)

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review,” J. Pharm. Biomed. Anal. 48, 533–553 (2008).
[CrossRef] [PubMed]

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

2007 (2)

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

J. Mallon and P. F. Whelan, “Calibration and removal of lateral chromatic aberration in images,” Pattern Recogn. Lett. 28, 125–135 (2007).
[CrossRef]

2006 (1)

P. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytometry Part A 69, 835–841 (2006).
[CrossRef]

2004 (1)

2003 (1)

V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003).
[PubMed]

1999 (2)

M. Unser, “Splines—a perfect fit for signal and image processing,” IEEE Signal Process Mag. 16, 22–38 (1999).
[CrossRef]

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

1992 (1)

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).
[CrossRef]

1989 (1)

F. L. Bookstein, “Principal warps: thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989).
[CrossRef]

1985 (1)

A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef] [PubMed]

1971 (1)

D. C. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).

Bannon, D.

D. Bannon, “Hyperspectral imaging: cubes and slices,” Nat. Photonics 3, 627–629 (2009).
[CrossRef]

Beach, J.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Bigler, S.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Bookstein, F. L.

F. L. Bookstein, “Principal warps: thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989).
[CrossRef]

Brakhage, P.

Bremer, C.

V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003).
[PubMed]

Brown, D. C.

D. C. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).

Bürmen, M.

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010).
[CrossRef]

Cohen, P.

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).
[CrossRef]

Collet, C.

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review,” J. Pharm. Biomed. Anal. 48, 533–553 (2008).
[CrossRef] [PubMed]

Cullen, P.

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

Downey, G.

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

Faruque, F.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Frias, J.

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

Gendrin, C.

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review,” J. Pharm. Biomed. Anal. 48, 533–553 (2008).
[CrossRef] [PubMed]

Goetz, A. F.

A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef] [PubMed]

Gowen, A.

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

Harris, C.

C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the Fourth Alvey Vision Conference (University of Manchester, 1988), pp. 147–152.

Hawkes, D. J.

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

Hayes, C.

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

Herniou, M.

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).
[CrossRef]

Hill, D. L.

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

Hughson, M.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Johnson, W.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Kasili, P.

P. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytometry Part A 69, 835–841 (2006).
[CrossRef]

Katrašnik, J.

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010).
[CrossRef]

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
[CrossRef]

Kelley, C. T.

C. T. Kelley, Iterative Methods for Optimization (Society for Industrial Mathematics, 1999).
[CrossRef]

Kowarschik, R.

Lai, K.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Leach, M. O.

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

Li, H.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Likar, B.

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010).
[CrossRef]

Machihin, A.

A. Machihin and V. Pozhar, “A spectral distortion correction method for an imaging spectrometer,” Instrum. Exp. Tech. 52, 847–853 (2009).
[CrossRef]

Mallon, J.

J. Mallon and P. F. Whelan, “Calibration and removal of lateral chromatic aberration in images,” Pattern Recogn. Lett. 28, 125–135 (2007).
[CrossRef]

Notni, G.

Ntziachristos, V.

V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003).
[PubMed]

O’Donnell, C.

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

Pernuš, F.

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
[CrossRef]

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010).
[CrossRef]

Pozhar, V.

A. Machihin and V. Pozhar, “A spectral distortion correction method for an imaging spectrometer,” Instrum. Exp. Tech. 52, 847–853 (2009).
[CrossRef]

Ricolfe-Viala, C.

C. Ricolfe-Viala and A. Sánchez-Salmerón, “Robust metric calibration of non-linear camera lens distortion,” Pattern Recogn. 43, 1688–1699 (2010).
[CrossRef]

Rock, B. N.

A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef] [PubMed]

Roggo, Y.

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review,” J. Pharm. Biomed. Anal. 48, 533–553 (2008).
[CrossRef] [PubMed]

Rueckert, D.

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

Sánchez-Salmerón, A.

C. Ricolfe-Viala and A. Sánchez-Salmerón, “Robust metric calibration of non-linear camera lens distortion,” Pattern Recogn. 43, 1688–1699 (2010).
[CrossRef]

Siddiqi, A.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Solomon, J. E.

A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef] [PubMed]

Sonoda, L. I.

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

Špiclin, Ž.

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
[CrossRef]

Stephens, M.

C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the Fourth Alvey Vision Conference (University of Manchester, 1988), pp. 147–152.

Unser, M.

M. Unser, “Splines—a perfect fit for signal and image processing,” IEEE Signal Process Mag. 16, 22–38 (1999).
[CrossRef]

Vane, G.

A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef] [PubMed]

Vo-Dinh, T.

P. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytometry Part A 69, 835–841 (2006).
[CrossRef]

Weissleder, R.

V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003).
[PubMed]

Weng, J.

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).
[CrossRef]

Whelan, P. F.

J. Mallon and P. F. Whelan, “Calibration and removal of lateral chromatic aberration in images,” Pattern Recogn. Lett. 28, 125–135 (2007).
[CrossRef]

Williams, W.

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Willson, R.

R. Willson, “Modeling and calibration of automated zoom lenses,” Ph.D. dissertation (Carnegie Mellon, 1994).

Appl. Opt. (1)

Cancer Cytopathol. (1)

A. Siddiqi, H. Li, F. Faruque, W. Williams, K. Lai, M. Hughson, S. Bigler, J. Beach, and W. Johnson, “Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells,” Cancer Cytopathol. 114, 13–21 (2008).
[CrossRef]

Chemom. Intell. Lab. Syst. (1)

J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging systems,” Chemom. Intell. Lab. Syst. 101, 23–29 (2010).
[CrossRef]

Cytometry Part A (1)

P. Kasili and T. Vo-Dinh, “Hyperspectral imaging system using acousto-optic tunable filter for flow cytometry applications,” Cytometry Part A 69, 835–841 (2006).
[CrossRef]

Eur. Radiol. (1)

V. Ntziachristos, C. Bremer, and R. Weissleder, “Fluorescence imaging with near-infrared light: new technological advances that enable in vivo molecular imaging,” Eur. Radiol. 13, 195–208 (2003).
[PubMed]

IEEE Signal Process Mag. (1)

M. Unser, “Splines—a perfect fit for signal and image processing,” IEEE Signal Process Mag. 16, 22–38 (1999).
[CrossRef]

IEEE Trans. Med. Imaging (1)

D. Rueckert, L. I. Sonoda, C. Hayes, D. L. Hill, M. O. Leach, and D. J. Hawkes, “Nonrigid registration using free-form deformations: application to breast MR images,” IEEE Trans. Med. Imaging 18, 712–721 (1999).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

F. L. Bookstein, “Principal warps: thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell. 11, 567–585 (1989).
[CrossRef]

J. Weng, P. Cohen, and M. Herniou, “Camera calibration with distortion models and accuracy evaluation,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 965–980 (1992).
[CrossRef]

Instrum. Exp. Tech. (1)

A. Machihin and V. Pozhar, “A spectral distortion correction method for an imaging spectrometer,” Instrum. Exp. Tech. 52, 847–853 (2009).
[CrossRef]

J. Pharm. Biomed. Anal. (1)

C. Gendrin, Y. Roggo, and C. Collet, “Pharmaceutical applications of vibrational chemical imaging and chemometrics: a review,” J. Pharm. Biomed. Anal. 48, 533–553 (2008).
[CrossRef] [PubMed]

Nat. Photonics (1)

D. Bannon, “Hyperspectral imaging: cubes and slices,” Nat. Photonics 3, 627–629 (2009).
[CrossRef]

Pattern Recogn. (1)

C. Ricolfe-Viala and A. Sánchez-Salmerón, “Robust metric calibration of non-linear camera lens distortion,” Pattern Recogn. 43, 1688–1699 (2010).
[CrossRef]

Pattern Recogn. Lett. (1)

J. Mallon and P. F. Whelan, “Calibration and removal of lateral chromatic aberration in images,” Pattern Recogn. Lett. 28, 125–135 (2007).
[CrossRef]

Photogramm. Eng. (1)

D. C. Brown, “Close-range camera calibration,” Photogramm. Eng. 37, 855–866 (1971).

Proc. SPIE (1)

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometrical calibration of an AOTF hyper-spectral imaging system,” Proc. SPIE 7556, 75560I (2010).
[CrossRef]

Science (1)

A. F. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
[CrossRef] [PubMed]

Trends Food Sci. Technol. (1)

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

Other (3)

C. T. Kelley, Iterative Methods for Optimization (Society for Industrial Mathematics, 1999).
[CrossRef]

C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the Fourth Alvey Vision Conference (University of Manchester, 1988), pp. 147–152.

R. Willson, “Modeling and calibration of automated zoom lenses,” Ph.D. dissertation (Carnegie Mellon, 1994).

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Figures (4)

Fig. 1
Fig. 1

(a) Line grid calibration object from Edmund Optics was imaged in the spectral range from 1.0 to 1.7 μm . (b) Spectral image at 1.35 μm from which a linear image segment was selected and (c) corresponding intensity profiles shown with respect to (w.r.t.) wavelength. Shifting the intensity profiles clearly demonstrates the wavelength dependent optical aberrations. (d) Intensity profiles from the same linear image segment after running the proposed method for geometric calibration.

Fig. 2
Fig. 2

(a) Hyperspectral imaging system composed of an InGaAs camera, an AOTF filter, an objective lens, and halogen lamps. (b) Set of 100 2D images of the calibration object acquired in the spectral range from 1.0 to 1.7 μm , forming a 320 × 256 × 100 spectral data cube. (c) Validation points used to evaluate and validate the performance of the proposed geometric calibration methods.

Fig. 3
Fig. 3

Results of the proposed geometric calibration tested on the AOTF-based hyperspectral imaging system with a Nikon or a Navitar lens. (a) 2D plots show the distribution of the remaining misalignments, and (b) the 1D plot shows the maximal remaining misalignment w.r.t. wavelength λ.

Fig. 4
Fig. 4

Variations of image quality parameters w.r.t. wavelength: (a) SNR and (b) the spatial extent of the PSF, i.e., standard deviation σ of the 2D Gaussian function.

Equations (8)

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u = a 1 x + a 2 y + a 3 a 7 x + a 8 y + 1 , v = a 4 x + a 5 y + a 6 a 7 x + a 8 y + 1 ,
p c = p d + δ ( p d , γ ) ,
T ( u , v , λ ) = l = 0 3 m = 0 3 n = 0 3 B l ( u ) B m ( v ) B n ( λ ) ϕ i + l , j + m , k + n ,
ς = ρ ( T ( u , v , λ ) ) α ϑ ( T ) ,
ρ ( T ( u , v , λ ) ) = λ u , v ( I ( T ( u , v , λ ) ) I ¯ ) ( I REF ( u , v ) I ¯ REF ) [ u , v ( I ( T ( u , v , λ ) ) I ¯ ) 2 u , v ( I REF ( u , v ) I ¯ REF ) 2 ] 1 / 2 .
ϑ ( T ) = u v λ [ ( 2 T u 2 ) 2 + ( 2 T v 2 ) 2 + ( 2 T u v ) 2 ] d u d v d λ ,
ε i λ = [ ( u i REF u i λ ) 2 + ( v i REF v i λ ) 2 ] 1 / 2 , i = 1 N ,
SNR ( λ ) [ dB ] = 10 log 10 ( | I ¯ λ ( p 1 ) I ¯ λ ( p 2 ) | 2 var ( I λ , DARK ) ) ,

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