Abstract

The accuracy of the radiometric response of acousto-optic tunable filter (AOTF) hyperspectral imaging systems is crucial for obtaining reliable measurements. It is therefore important to know the radiometric response and noise characteristics of the hyperspectral imaging system used. A radiometric model of an AOTF hyperspectral imaging system composed of an imaging sensor radiometric model (CCD, CMOS, and sCMOS) and an AOTF light transmission model is proposed. Using the radiometric model, a method for obtaining the fixed pattern noise (FPN) of the imaging system by displacing and imaging an illuminated reference target is developed. Methods for estimating the temporal noise of the imaging system, using the photon transfer method, and for correcting FPN are also presented. Noise estimation and image restoration methods were tested on an AOTF hyperspectral imaging system. The results indicate that the developed methods can accurately calculate temporal and FPN, and can effectively correct the acquired images. After correction, the signal-to-noise ratio of the acquired images was shown to increase by 26%.

© 2013 Optical Society of America

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[CrossRef]

H. Liang, “Advances in multispectral and hyperspectral imaging for archaeology and art conservation,” Appl. Phys. A 106, 309–323 (2012).
[CrossRef]

2011

J. A. Chin, E. C. Wang, and M. R. Kibbe, “Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease,” J. Vasc. Surg. 54, 1679–1688 (2011).
[CrossRef]

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102, 852–857 (2011).
[CrossRef]

J. Prats-Montalbán, A. de Juan, and A. Ferrer, “Multivariate image analysis: a review with applications,” Chemom. Intell. Lab. Syst. 107, 1–23 (2011).
[CrossRef]

M. Bürmen, F. Pernuš, and B. Likar, “Spectral characterization of near-infrared acousto-optic tunable filter (AOTF) hyperspectral imaging systems using standard calibration materials,” Appl. Spectrosc. 65, 393–401 (2011).
[CrossRef]

T. Skauli, “Sensor noise informed representation of hyperspectral data, with benefits for image storage and processing,” Opt. Express 19, 13031–13046 (2011).
[CrossRef]

2010

Ž. Špiclin, J. Katrašnik, M. Bürmen, F. Pernuš, and B. Likar, “Geometric calibration of a hyperspectral imaging system,” Appl. Opt. 49, 2813–2818 (2010).
[CrossRef]

J. Amigo, “Practical issues of hyperspectral imaging analysis of solid dosage forms,” Anal. Bioanal. Chem. 398, 93–109 (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]

J. Vila-Francés, J. Calpe-Maravilla, L. Gómez-Chova, and J. Amorós-López, “Improving the performance of acousto-optic tunable filters in imaging applications,” J. Electron. Imaging 19, 043022 (2010).
[CrossRef]

2009

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

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

2008

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]

N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
[CrossRef]

2007

B. Penna, T. Tillo, and E. Magli, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[CrossRef]

A. Gowen, C. Odonnell, 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]

2006

H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).
[CrossRef]

2005

A. E. Gamal and H. Eltoukhy, “CMOS image sensors,” IEEE Circuits Devices Mag. 21(3), 6–20 (2005).
[CrossRef]

2004

2003

J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
[CrossRef]

2002

2001

H. Tian, B. Fowler, and A. E. Gamal, “Analysis of temporal noise in CMOS photodiode active pixel sensor,” IEEE J. Solid-State Circuits 36, 92–101 (2001).
[CrossRef]

2000

B. Likar, J. Maintz, and M. Viergever, “Retrospective shading correction based on entropy minimization,” J. Microsc. 197, 285–295 (2000).
[CrossRef]

N. Gat, “Imaging spectroscopy using tunable filters: a review,” Proc. SPIE 4056, 50–64 (2000).
[CrossRef]

1999

P. C. Knee, “Investigation of the uniformity and ageing of integrating spheres,” Anal. Chim. Acta 380, 391–399 (1999).
[CrossRef]

1994

G. E. Healey and R. Kondepudy, “Radiometric CCD camera calibration and noise estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 267–276 (1994).
[CrossRef]

1985

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

Akbari, H.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102, 852–857 (2011).
[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, 1121–1142 (2012).
[CrossRef]

Amigo, J.

J. Amigo, “Practical issues of hyperspectral imaging analysis of solid dosage forms,” Anal. Bioanal. Chem. 398, 93–109 (2010).
[CrossRef]

Amorós-López, J.

J. Vila-Francés, J. Calpe-Maravilla, L. Gómez-Chova, and J. Amorós-López, “Improving the performance of acousto-optic tunable filters in imaging applications,” J. Electron. Imaging 19, 043022 (2010).
[CrossRef]

Bannon, D.

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

Barry, P. S.

J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
[CrossRef]

Beiso, D.

J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
[CrossRef]

Bellon-Maurel, V.

N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
[CrossRef]

Benediktsson, J. A.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Bissett, P.

Bissett, W.

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, 1121–1142 (2012).
[CrossRef]

Boardman, J. W.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Bowles, J.

Brazile, J.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Bruzzone, L.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Bürmen, M.

Calpe-Maravilla, J.

J. Vila-Francés, J. Calpe-Maravilla, L. Gómez-Chova, and J. Amorós-López, “Improving the performance of acousto-optic tunable filters in imaging applications,” J. Electron. Imaging 19, 043022 (2010).
[CrossRef]

Camps-Valls, G.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Carman, S. L.

J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
[CrossRef]

Chang, I.

I. Chang, “Acousto-optic devices and applications,” in Handbook of Optics: Devices, Measurements, and Properties, M. Bass, ed. 2nd ed. (McGraw-Hill, 1994), p. 1568.

Chanussot, J.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Chen, W.

Chin, J. A.

J. A. Chin, E. C. Wang, and M. R. Kibbe, “Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease,” J. Vasc. Surg. 54, 1679–1688 (2011).
[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]

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, 1121–1142 (2012).
[CrossRef]

Cullen, P.

A. Gowen, C. Odonnell, 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]

Davis, C.

de Juan, A.

J. Prats-Montalbán, A. de Juan, and A. Ferrer, “Multivariate image analysis: a review with applications,” Chemom. Intell. Lab. Syst. 107, 1–23 (2011).
[CrossRef]

Downes, T. V.

Downey, G.

A. Gowen, C. Odonnell, 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]

Eltoukhy, H.

A. E. Gamal and H. Eltoukhy, “CMOS image sensors,” IEEE Circuits Devices Mag. 21(3), 6–20 (2005).
[CrossRef]

Fauvel, M.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Ferrer, A.

J. Prats-Montalbán, A. de Juan, and A. Ferrer, “Multivariate image analysis: a review with applications,” Chemom. Intell. Lab. Syst. 107, 1–23 (2011).
[CrossRef]

Fiorio, C.

N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
[CrossRef]

Fisher, J.

Fowler, B.

H. Tian, B. Fowler, and A. E. Gamal, “Analysis of temporal noise in CMOS photodiode active pixel sensor,” IEEE J. Solid-State Circuits 36, 92–101 (2001).
[CrossRef]

Frias, J.

A. Gowen, C. Odonnell, 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]

Gamal, A. E.

A. E. Gamal and H. Eltoukhy, “CMOS image sensors,” IEEE Circuits Devices Mag. 21(3), 6–20 (2005).
[CrossRef]

H. Tian, B. Fowler, and A. E. Gamal, “Analysis of temporal noise in CMOS photodiode active pixel sensor,” IEEE J. Solid-State Circuits 36, 92–101 (2001).
[CrossRef]

Gamba, P.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[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, 1121–1142 (2012).
[CrossRef]

Gat, N.

N. Gat, “Imaging spectroscopy using tunable filters: a review,” Proc. SPIE 4056, 50–64 (2000).
[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]

Goetz, A. F. H.

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

Gómez-Chova, L.

J. Vila-Francés, J. Calpe-Maravilla, L. Gómez-Chova, and J. Amorós-López, “Improving the performance of acousto-optic tunable filters in imaging applications,” J. Electron. Imaging 19, 043022 (2010).
[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, 1121–1142 (2012).
[CrossRef]

Gorretta, N.

N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
[CrossRef]

Gowen, A.

A. Gowen, C. Odonnell, 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]

Gualtieri, A.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

Healey, G. E.

G. E. Healey and R. Kondepudy, “Radiometric CCD camera calibration and noise estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 267–276 (1994).
[CrossRef]

Hodges, J. L.

J. L. Hodges and E. L. Lehmann, Basic Concepts of Probability and Statistics, Classics in Applied Mathematics (Society for Industrial and Applied Mathematics, 1970), Vol 48.

Janesick, J. R.

J. R. Janesick, Photon Transfer (SPIE, 2007).

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, “Geometric calibration of a hyperspectral imaging system,” Appl. Opt. 49, 2813–2818 (2010).
[CrossRef]

Kibbe, M. R.

J. A. Chin, E. C. Wang, and M. R. Kibbe, “Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease,” J. Vasc. Surg. 54, 1679–1688 (2011).
[CrossRef]

Knee, P. C.

P. C. Knee, “Investigation of the uniformity and ageing of integrating spheres,” Anal. Chim. Acta 380, 391–399 (1999).
[CrossRef]

Kohler, D.

Kojima, K.

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[CrossRef]

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G. E. Healey and R. Kondepudy, “Radiometric CCD camera calibration and noise estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 267–276 (1994).
[CrossRef]

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H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102, 852–857 (2011).
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N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
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H. Liang, “Advances in multispectral and hyperspectral imaging for archaeology and art conservation,” Appl. Phys. A 106, 309–323 (2012).
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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, 1121–1142 (2012).
[CrossRef]

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B. Penna, T. Tillo, and E. Magli, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
[CrossRef]

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B. Likar, J. Maintz, and M. Viergever, “Retrospective shading correction based on entropy minimization,” J. Microsc. 197, 285–295 (2000).
[CrossRef]

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A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
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R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and Statistics for Engineers and Scientists (Pearson Education, 2010).

Odonnell, C.

A. Gowen, C. Odonnell, 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]

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H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).
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J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
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B. Penna, T. Tillo, and E. Magli, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
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A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
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J. Prats-Montalbán, A. de Juan, and A. Ferrer, “Multivariate image analysis: a review with applications,” Chemom. Intell. Lab. Syst. 107, 1–23 (2011).
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H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).
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N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
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N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
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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).
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J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
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J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
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H. Tian, B. Fowler, and A. E. Gamal, “Analysis of temporal noise in CMOS photodiode active pixel sensor,” IEEE J. Solid-State Circuits 36, 92–101 (2001).
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H. Tian, “Noise analysis in CMOS image sensors,” Ph.D. thesis (Stanford University, 2000).

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B. Penna, T. Tillo, and E. Magli, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
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A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
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A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ. 113, S110–S122 (2009).
[CrossRef]

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H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102, 852–857 (2011).
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F. Vagni, “Survey of hyperspectral and multispectral imaging technologies,” Technical report (NATO, 2007).

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A. F. H. Goetz, G. Vane, J. E. Solomon, and B. N. Rock, “Imaging spectrometry for Earth remote sensing,” Science 228, 1147–1153 (1985).
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B. Likar, J. Maintz, and M. Viergever, “Retrospective shading correction based on entropy minimization,” J. Microsc. 197, 285–295 (2000).
[CrossRef]

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J. Vila-Francés, J. Calpe-Maravilla, L. Gómez-Chova, and J. Amorós-López, “Improving the performance of acousto-optic tunable filters in imaging applications,” J. Electron. Imaging 19, 043022 (2010).
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J. A. Chin, E. C. Wang, and M. R. Kibbe, “Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease,” J. Vasc. Surg. 54, 1679–1688 (2011).
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R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and Statistics for Engineers and Scientists (Pearson Education, 2010).

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H. Liang, “Advances in multispectral and hyperspectral imaging for archaeology and art conservation,” Appl. Phys. A 106, 309–323 (2012).
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Appl. Spectrosc.

Cancer Sci.

H. Akbari, K. Uto, Y. Kosugi, K. Kojima, and N. Tanaka, “Cancer detection using infrared hyperspectral imaging,” Cancer Sci. 102, 852–857 (2011).
[CrossRef]

Chemom. Intell. Lab. Syst.

J. Prats-Montalbán, A. de Juan, and A. Ferrer, “Multivariate image analysis: a review with applications,” Chemom. Intell. Lab. Syst. 107, 1–23 (2011).
[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]

Food Bioprocess Technol.

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, 1121–1142 (2012).
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IEEE Circuits Devices Mag.

A. E. Gamal and H. Eltoukhy, “CMOS image sensors,” IEEE Circuits Devices Mag. 21(3), 6–20 (2005).
[CrossRef]

IEEE J. Solid-State Circuits

H. Tian, B. Fowler, and A. E. Gamal, “Analysis of temporal noise in CMOS photodiode active pixel sensor,” IEEE J. Solid-State Circuits 36, 92–101 (2001).
[CrossRef]

IEEE Trans. Geosci. Remote Sens.

B. Penna, T. Tillo, and E. Magli, “Transform coding techniques for lossy hyperspectral data compression,” IEEE Trans. Geosci. Remote Sens. 45, 1408–1421 (2007).
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H. Othman and S. Qian, “Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage,” IEEE Trans. Geosci. Remote Sens. 44, 397–408 (2006).
[CrossRef]

J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso, and S. L. Carman, “Hyperion, a space-based imaging spectrometer,” IEEE Trans. Geosci. Remote Sens. 41, 1160–1173 (2003).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

G. E. Healey and R. Kondepudy, “Radiometric CCD camera calibration and noise estimation,” IEEE Trans. Pattern Anal. Mach. Intell. 16, 267–276 (1994).
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J. Vila-Francés, J. Calpe-Maravilla, L. Gómez-Chova, and J. Amorós-López, “Improving the performance of acousto-optic tunable filters in imaging applications,” J. Electron. Imaging 19, 043022 (2010).
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B. Likar, J. Maintz, and M. Viergever, “Retrospective shading correction based on entropy minimization,” J. Microsc. 197, 285–295 (2000).
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J. Near Infrared Spectrosc.

N. Gorretta, G. Rabatel, J.-M. Roger, C. Fiorio, C. Lelong, and V. Bellon-Maurel, “Hyperspectral imaging system calibration using image translations and Fourier transform,” J. Near Infrared Spectrosc. 16, 371–380 (2008).
[CrossRef]

J. Pharm. Biomed. Anal.

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]

J. Vasc. Surg.

J. A. Chin, E. C. Wang, and M. R. Kibbe, “Evaluation of hyperspectral technology for assessing the presence and severity of peripheral artery disease,” J. Vasc. Surg. 54, 1679–1688 (2011).
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Science

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Trends Food Sci. Technol.

A. Gowen, C. Odonnell, 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).
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F. Vagni, “Survey of hyperspectral and multispectral imaging technologies,” Technical report (NATO, 2007).

R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and Statistics for Engineers and Scientists (Pearson Education, 2010).

H. Tian, “Noise analysis in CMOS image sensors,” Ph.D. thesis (Stanford University, 2000).

J. L. Hodges and E. L. Lehmann, Basic Concepts of Probability and Statistics, Classics in Applied Mathematics (Society for Industrial and Applied Mathematics, 1970), Vol 48.

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Figures (11)

Fig. 1.
Fig. 1.

Image of the reference target used for acquiring images for FPNg calibration.

Fig. 2.
Fig. 2.

Constructing the FPNg image from acquired images.

Fig. 3.
Fig. 3.

Flow chart for calculating the FPNg approximation image α^i,j,k.

Fig. 4.
Fig. 4.

Standard deviation of TN in dependence with the average number of photon generated electrons multiplied with camera gain across all pixels GS¯. The blue line with circles represents the results obtained using Eq. (21), while the solid green line was obtained using Eq. (20) and the reference camera gain.

Fig. 5.
Fig. 5.

Histogram of a dark image corrected for FPNo, representing the TNc. Each bin is one quantization unit (q) wide. The counts are normalized to the number of all pixels.

Fig. 6.
Fig. 6.

FPNo of the hyperspectral imaging system. The contrast of the image was artificially increased in order to improve the visibility of the pattern.

Fig. 7.
Fig. 7.

FPNg at wavelengths of 660, 740, and 820 nm. The images were obtained using the method for FPNg estimation. The scales on the colorbars are in relative image intensity.

Fig. 8.
Fig. 8.

Spectra of FPNg at three different pixel positions [see Fig. 7(a)] and the spectrum of the image mean.

Fig. 9.
Fig. 9.

Images before correction. The black rectangle in the left image denotes the part of the image that was used to validate the correction method. The histogram in the bottom left corner of the images represents the whole image histogram.

Fig. 10.
Fig. 10.

Images after correction.

Fig. 11.
Fig. 11.

Image profiles. The positions of the profiles in the images are shown in Figs. 9 and 10.

Tables (2)

Tables Icon

Table 1. Optical Density and Transmission at 740 nm of Neutral Density Filters Used

Tables Icon

Table 2. Correction Results

Equations (31)

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

Si,j=teλyxP(x,y,λ)qi,j(λ)dxdydλ,
Si,j=teAi,jλpi,j(λ)qi,j(λ)dλ,
Si,j=ki,jλpi,j(λ)q(λ)dλ.
Ii,j=(Si,j+Ns+Pd+Np+Nr)Gi,j+Uq.
FPNoi,j=(μs+ρd)Gi,j.
FPNgi,j=ki,jGi,j.
TN=N(0,σp2+σs2+ρd+σr2+q212).
TN=TNc+TNv(σp2).
Ii,j=FPNgi,jλpi,j(λ)q(λ)dλ+FPNoi,j+TN.
T=T0sinc2(λλc4.5152r(λc)),
T0=sin2(π22λc2M2PaL2).
f=vΔnsin4θi+sin22θiλc.
T=T(x,y,λ,f,Pa).
Si,j=teλyxT(x,y,λ,f,Pa)·P(x,y,λ)qi,j(λ)dxdydλ.
Si,j(fk)=teAi,jpi,j(λk)qi,j(λk)λTi,j(λ,fk)dλ,
Si,j,k=teAi,jpi,j,kqi,j,kτi,j,k,
αi,j,k=teAi,jqi,j,kτi,j,kGi,j,
Hi,j,k=αi,j,kpi,j,k+FPNoi,j+TN.
std[TNci,j]=1M1m=0M1(di,j(m)d¯i,j)2,
std[TNvi,j,k]=Gi,jSi,j,k.
std[TNvi,j,k]=std[totali,j,k]2std[TNci,j]2.
Gi,j=std[TNvi,j,k]Hi,j,k,
FPNoi,j=1Mm=0M1di,j(m).
α^i,j,k=cαi,j,k.
Ri,j,k=Hi,j,kFPNoi,jα^i,j,k(1).
Hi,j,kc=Hi,j,kFPNoi,jri,j,k(1).
ptarget=ϕtargetApixel.
αi,j,k=α^i,j,kptarget.
Ri,j,k=Hi,j,kFPNoi,jα^i,j,k,
SNR(Ri,j,k)=SNR(Ii,j,k)SNR(α^i,j,k)SNR(Ii,j,k)2+SNR(α^i,j,k)2,
SNR(Ii,j,k)=μIσI,SNR(α^i,j,k)=μα^σα^.

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