Abstract

An optimized method for spectral reflectance reconstruction from digital camera responses is proposed by selecting and weighting the training samples. The proposed method was based on camera responses expansion and pseudo inverse operation. Local optimal training samples were selected for each testing sample. Weighting coefficient matrix was calculated for local optimal training samples to improve the reconstruction accuracy. Experimental results indicate that the proposed method significantly outperforms currently existing methods in terms of both spectral and colorimetric accuracy, and the proposed optimized strategy was integrated well with those counterpart methods to improve their reconstruction accuracy.

© 2017 Optical Society of America

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References

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  1. D. F. Barbin, G. ElMasry, D. W. Sun, and P. Allen, “Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging,” Anal. Chim. Acta 719(10), 30–42 (2012).
    [PubMed]
  2. S. Helling, E. Seidel, and W. Biehlig, “Algorithms for spectral color stimulus reconstruction with a seven-channel multispectral camera,” in Conference on Color in Graphics, Imaging and Vision (2004), pp. 254–258.
  3. S. A. Mathews, “Design and fabrication of a low-cost, multispectral imaging system,” Appl. Opt. 47(28), F71–F76 (2008).
    [PubMed]
  4. S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” in Asian Conference on Computer Vision (2010), pp. 323–335.
  5. J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41(10), 2532–2548 (2002).
  6. R. S. Berns and L. A. Taplin, “Practical Spectral Imaging Using a Color-Filter Array Digital Camera,” (Final Report, 2006), http://art-si.org/PDFs/Acquisition/TR_Practical_Spectral_Imaging.pdf .
  7. J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).
  8. P. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).
  9. S. Tominaga and N. Tanaka, “Spectral image acquisition, analysis, and rendering for art paintings,” J. Electron. Imaging 17(4), 043022 (2008).
  10. N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).
  11. H. Liang, “Advances in multispectral and hyperspectral imaging for archaeology and art conservation,” Appl. Phys., A Mater. Sci. Process. 106(2), 309–323 (2012).
  12. J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: geometric distortion model and compensation algorithms,” IEEE Trans. Image Process. 17(12), 2368–2380 (2008).
    [PubMed]
  13. D. Connah and J. Y. Hardeberg, “Spectral recovery using polynomial models,” Proc. SPIE 5667, 65–75 (2005).
  14. V. Heikkinen, T. Jetsu, J. Parkkinen, M. Hauta-Kasari, T. Jaaskelainen, and S. D. Lee, “Regularized learning framework in the estimation of reflectance spectra from camera responses,” J. Opt. Soc. Am. A 24(9), 2673–2683 (2007).
    [PubMed]
  15. H. Shen, H. Wan, and Z. Zhang, “Estimating reflectance from multispectral camera responses based on partial least-squares regression,” J. Electron. Imaging 19(2), 020501 (2010).
  16. X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).
  17. K. Xiao, Y. Zhu, C. Li, D. Connah, J. M. Yates, and S. Wuerger, “Improved method for skin reflectance reconstruction from camera images,” Opt. Express 24(13), 14934–14950 (2016).
    [PubMed]
  18. H. Li, Z. Wu, L. Zhang, and J. Parkkinen, “SR-LLA: A novel spectral reconstruction method based on locally linear approximation,” inProceedings of IEEE Conference on Image Processing (IEEE, 2013), pp. 2029–2033.
  19. B. Cao, N. Liao, and H. Cheng, “Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group,” Color Res. Appl. 42(3), 327–332 (2017).
  20. C. Li, G. Cui, and M. R. Luo, “The accuracy of polynomial models for characterizing digital cameras,” in Proceedings of AIC2003 Bangkok: Color Communication and Management (2003), pp. 166–170.
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    [PubMed]
  22. Y. Zhao and R. S. Berns, “Image-based spectral reflectance reconstruction using the matrix R method,” Color Res. Appl. 32(5), 343–351 (2010).
  23. G. Latour, M. Elias, and J. M. Frigerio, “Determination of the absorption and scattering coefficients of pigments: application to the identification of the components of pigment mixtures,” Appl. Spectrosc. 63(6), 604–610 (2009).
    [PubMed]
  24. V. Cheung and S. Westland, “Methods for optimal color selection,” J. Imaging Sci. Technol. 50(5), 481–488 (2006).
  25. B. M. R. Luo, G. Cui, and B. Rigg, “The development of the CIE 2000 colour‐difference formula: CIEDE2000,” Color Res. Appl. 26(5), 340–350 (2001).

2017 (2)

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

B. Cao, N. Liao, and H. Cheng, “Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group,” Color Res. Appl. 42(3), 327–332 (2017).

2016 (2)

J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).

K. Xiao, Y. Zhu, C. Li, D. Connah, J. M. Yates, and S. Wuerger, “Improved method for skin reflectance reconstruction from camera images,” Opt. Express 24(13), 14934–14950 (2016).
[PubMed]

2012 (2)

H. Liang, “Advances in multispectral and hyperspectral imaging for archaeology and art conservation,” Appl. Phys., A Mater. Sci. Process. 106(2), 309–323 (2012).

D. F. Barbin, G. ElMasry, D. W. Sun, and P. Allen, “Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging,” Anal. Chim. Acta 719(10), 30–42 (2012).
[PubMed]

2010 (3)

H. Shen, H. Wan, and Z. Zhang, “Estimating reflectance from multispectral camera responses based on partial least-squares regression,” J. Electron. Imaging 19(2), 020501 (2010).

P. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).

Y. Zhao and R. S. Berns, “Image-based spectral reflectance reconstruction using the matrix R method,” Color Res. Appl. 32(5), 343–351 (2010).

2009 (2)

G. Latour, M. Elias, and J. M. Frigerio, “Determination of the absorption and scattering coefficients of pigments: application to the identification of the components of pigment mixtures,” Appl. Spectrosc. 63(6), 604–610 (2009).
[PubMed]

N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).

2008 (3)

S. Tominaga and N. Tanaka, “Spectral image acquisition, analysis, and rendering for art paintings,” J. Electron. Imaging 17(4), 043022 (2008).

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: geometric distortion model and compensation algorithms,” IEEE Trans. Image Process. 17(12), 2368–2380 (2008).
[PubMed]

S. A. Mathews, “Design and fabrication of a low-cost, multispectral imaging system,” Appl. Opt. 47(28), F71–F76 (2008).
[PubMed]

2007 (2)

2006 (1)

V. Cheung and S. Westland, “Methods for optimal color selection,” J. Imaging Sci. Technol. 50(5), 481–488 (2006).

2005 (1)

D. Connah and J. Y. Hardeberg, “Spectral recovery using polynomial models,” Proc. SPIE 5667, 65–75 (2005).

2002 (1)

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41(10), 2532–2548 (2002).

2001 (1)

B. M. R. Luo, G. Cui, and B. Rigg, “The development of the CIE 2000 colour‐difference formula: CIEDE2000,” Color Res. Appl. 26(5), 340–350 (2001).

Aach, T.

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: geometric distortion model and compensation algorithms,” IEEE Trans. Image Process. 17(12), 2368–2380 (2008).
[PubMed]

Allen, P.

D. F. Barbin, G. ElMasry, D. W. Sun, and P. Allen, “Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging,” Anal. Chim. Acta 719(10), 30–42 (2012).
[PubMed]

Barbin, D. F.

D. F. Barbin, G. ElMasry, D. W. Sun, and P. Allen, “Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging,” Anal. Chim. Acta 719(10), 30–42 (2012).
[PubMed]

Berns, R. S.

Y. Zhao and R. S. Berns, “Image-based spectral reflectance reconstruction using the matrix R method,” Color Res. Appl. 32(5), 343–351 (2010).

Biehlig, W.

S. Helling, E. Seidel, and W. Biehlig, “Algorithms for spectral color stimulus reconstruction with a seven-channel multispectral camera,” in Conference on Color in Graphics, Imaging and Vision (2004), pp. 254–258.

Brauers, J.

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: geometric distortion model and compensation algorithms,” IEEE Trans. Image Process. 17(12), 2368–2380 (2008).
[PubMed]

Brettel, H.

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41(10), 2532–2548 (2002).

Calcagni, A. S.

N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).

Cao, B.

B. Cao, N. Liao, and H. Cheng, “Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group,” Color Res. Appl. 42(3), 327–332 (2017).

Cheng, H.

B. Cao, N. Liao, and H. Cheng, “Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group,” Color Res. Appl. 42(3), 327–332 (2017).

Cheung, V.

V. Cheung and S. Westland, “Methods for optimal color selection,” J. Imaging Sci. Technol. 50(5), 481–488 (2006).

Claridge, E.

N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).

Connah, D.

Cui, G.

B. M. R. Luo, G. Cui, and B. Rigg, “The development of the CIE 2000 colour‐difference formula: CIEDE2000,” Color Res. Appl. 26(5), 340–350 (2001).

C. Li, G. Cui, and M. R. Luo, “The accuracy of polynomial models for characterizing digital cameras,” in Proceedings of AIC2003 Bangkok: Color Communication and Management (2003), pp. 166–170.

Elias, M.

ElMasry, G.

D. F. Barbin, G. ElMasry, D. W. Sun, and P. Allen, “Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging,” Anal. Chim. Acta 719(10), 30–42 (2012).
[PubMed]

Everdell, N. L.

N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).

Frigerio, J. M.

Han, S.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” in Asian Conference on Computer Vision (2010), pp. 323–335.

Hardeberg, J. Y.

D. Connah and J. Y. Hardeberg, “Spectral recovery using polynomial models,” Proc. SPIE 5667, 65–75 (2005).

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41(10), 2532–2548 (2002).

Hauta-Kasari, M.

Hebden, J. C.

N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).

Heikkinen, V.

Helling, S.

S. Helling, E. Seidel, and W. Biehlig, “Algorithms for spectral color stimulus reconstruction with a seven-channel multispectral camera,” in Conference on Color in Graphics, Imaging and Vision (2004), pp. 254–258.

Jaaskelainen, T.

Jetsu, T.

Latour, G.

Lee, S. D.

Li, C.

K. Xiao, Y. Zhu, C. Li, D. Connah, J. M. Yates, and S. Wuerger, “Improved method for skin reflectance reconstruction from camera images,” Opt. Express 24(13), 14934–14950 (2016).
[PubMed]

J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).

C. Li, G. Cui, and M. R. Luo, “The accuracy of polynomial models for characterizing digital cameras,” in Proceedings of AIC2003 Bangkok: Color Communication and Management (2003), pp. 166–170.

Li, H.

H. Li, Z. Wu, L. Zhang, and J. Parkkinen, “SR-LLA: A novel spectral reconstruction method based on locally linear approximation,” inProceedings of IEEE Conference on Image Processing (IEEE, 2013), pp. 2029–2033.

Li, J.

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).

Liang, H.

H. Liang, “Advances in multispectral and hyperspectral imaging for archaeology and art conservation,” Appl. Phys., A Mater. Sci. Process. 106(2), 309–323 (2012).

Liang, J.

J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).

Liao, N.

B. Cao, N. Liao, and H. Cheng, “Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group,” Color Res. Appl. 42(3), 327–332 (2017).

Liu, Q.

J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).

Luo, B. M. R.

B. M. R. Luo, G. Cui, and B. Rigg, “The development of the CIE 2000 colour‐difference formula: CIEDE2000,” Color Res. Appl. 26(5), 340–350 (2001).

Luo, M. R.

C. Li, G. Cui, and M. R. Luo, “The accuracy of polynomial models for characterizing digital cameras,” in Proceedings of AIC2003 Bangkok: Color Communication and Management (2003), pp. 166–170.

Mathews, S. A.

Okabe, T.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” in Asian Conference on Computer Vision (2010), pp. 323–335.

Parkkinen, J.

V. Heikkinen, T. Jetsu, J. Parkkinen, M. Hauta-Kasari, T. Jaaskelainen, and S. D. Lee, “Regularized learning framework in the estimation of reflectance spectra from camera responses,” J. Opt. Soc. Am. A 24(9), 2673–2683 (2007).
[PubMed]

H. Li, Z. Wu, L. Zhang, and J. Parkkinen, “SR-LLA: A novel spectral reconstruction method based on locally linear approximation,” inProceedings of IEEE Conference on Image Processing (IEEE, 2013), pp. 2029–2033.

Richardson, M.

P. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).

Rigg, B.

B. M. R. Luo, G. Cui, and B. Rigg, “The development of the CIE 2000 colour‐difference formula: CIEDE2000,” Color Res. Appl. 26(5), 340–350 (2001).

Sato, I.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” in Asian Conference on Computer Vision (2010), pp. 323–335.

Sato, Y.

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” in Asian Conference on Computer Vision (2010), pp. 323–335.

Schmitt, F.

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41(10), 2532–2548 (2002).

Schulte, N.

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: geometric distortion model and compensation algorithms,” IEEE Trans. Image Process. 17(12), 2368–2380 (2008).
[PubMed]

Seidel, E.

S. Helling, E. Seidel, and W. Biehlig, “Algorithms for spectral color stimulus reconstruction with a seven-channel multispectral camera,” in Conference on Color in Graphics, Imaging and Vision (2004), pp. 254–258.

Shao, S. J.

Shen, H.

H. Shen, H. Wan, and Z. Zhang, “Estimating reflectance from multispectral camera responses based on partial least-squares regression,” J. Electron. Imaging 19(2), 020501 (2010).

Shen, H. L.

Styles, I. B.

N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).

Sun, D. W.

D. F. Barbin, G. ElMasry, D. W. Sun, and P. Allen, “Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging,” Anal. Chim. Acta 719(10), 30–42 (2012).
[PubMed]

Tanaka, N.

S. Tominaga and N. Tanaka, “Spectral image acquisition, analysis, and rendering for art paintings,” J. Electron. Imaging 17(4), 043022 (2008).

Tominaga, S.

S. Tominaga and N. Tanaka, “Spectral image acquisition, analysis, and rendering for art paintings,” J. Electron. Imaging 17(4), 043022 (2008).

Wan, H.

H. Shen, H. Wan, and Z. Zhang, “Estimating reflectance from multispectral camera responses based on partial least-squares regression,” J. Electron. Imaging 19(2), 020501 (2010).

Wan, X.

J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).

Wang, Q.

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

Westland, S.

V. Cheung and S. Westland, “Methods for optimal color selection,” J. Imaging Sci. Technol. 50(5), 481–488 (2006).

Wu, Z.

H. Li, Z. Wu, L. Zhang, and J. Parkkinen, “SR-LLA: A novel spectral reconstruction method based on locally linear approximation,” inProceedings of IEEE Conference on Image Processing (IEEE, 2013), pp. 2029–2033.

Wuerger, S.

Xiao, K.

Xin, J. H.

Xu, H.

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

Yang, Y.

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

Yates, J. M.

Yuen, P.

P. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).

Zhang, L.

H. Li, Z. Wu, L. Zhang, and J. Parkkinen, “SR-LLA: A novel spectral reconstruction method based on locally linear approximation,” inProceedings of IEEE Conference on Image Processing (IEEE, 2013), pp. 2029–2033.

Zhang, X.

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

Zhang, Z.

H. Shen, H. Wan, and Z. Zhang, “Estimating reflectance from multispectral camera responses based on partial least-squares regression,” J. Electron. Imaging 19(2), 020501 (2010).

Zhao, Y.

Y. Zhao and R. S. Berns, “Image-based spectral reflectance reconstruction using the matrix R method,” Color Res. Appl. 32(5), 343–351 (2010).

Zhou, X.

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

Zhu, Y.

Anal. Chim. Acta (1)

D. F. Barbin, G. ElMasry, D. W. Sun, and P. Allen, “Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging,” Anal. Chim. Acta 719(10), 30–42 (2012).
[PubMed]

Appl. Opt. (1)

Appl. Phys., A Mater. Sci. Process. (1)

H. Liang, “Advances in multispectral and hyperspectral imaging for archaeology and art conservation,” Appl. Phys., A Mater. Sci. Process. 106(2), 309–323 (2012).

Appl. Spectrosc. (1)

Color Res. Appl. (5)

Y. Zhao and R. S. Berns, “Image-based spectral reflectance reconstruction using the matrix R method,” Color Res. Appl. 32(5), 343–351 (2010).

B. M. R. Luo, G. Cui, and B. Rigg, “The development of the CIE 2000 colour‐difference formula: CIEDE2000,” Color Res. Appl. 26(5), 340–350 (2001).

J. Liang, X. Wan, Q. Liu, C. Li, and J. Li, “Research on filter selection method for broadband spectral imaging system based on ancient murals,” Color Res. Appl. 41(6), 585–595 (2016).

X. Zhang, Q. Wang, J. Li, X. Zhou, Y. Yang, and H. Xu, “Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants,” Color Res. Appl. 42(1), 68–77 (2017).

B. Cao, N. Liao, and H. Cheng, “Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group,” Color Res. Appl. 42(3), 327–332 (2017).

IEEE Trans. Image Process. (1)

J. Brauers, N. Schulte, and T. Aach, “Multispectral filter-wheel cameras: geometric distortion model and compensation algorithms,” IEEE Trans. Image Process. 17(12), 2368–2380 (2008).
[PubMed]

Imaging Sci. J. (1)

P. Yuen and M. Richardson, “An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition,” Imaging Sci. J. 58(5), 241–253 (2010).

J. Electron. Imaging (2)

S. Tominaga and N. Tanaka, “Spectral image acquisition, analysis, and rendering for art paintings,” J. Electron. Imaging 17(4), 043022 (2008).

H. Shen, H. Wan, and Z. Zhang, “Estimating reflectance from multispectral camera responses based on partial least-squares regression,” J. Electron. Imaging 19(2), 020501 (2010).

J. Imaging Sci. Technol. (1)

V. Cheung and S. Westland, “Methods for optimal color selection,” J. Imaging Sci. Technol. 50(5), 481–488 (2006).

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

Opt. Eng. (1)

J. Y. Hardeberg, F. Schmitt, and H. Brettel, “Multispectral color image capture using a liquid crystal tunable filter,” Opt. Eng. 41(10), 2532–2548 (2002).

Opt. Express (2)

Proc. SPIE (2)

D. Connah and J. Y. Hardeberg, “Spectral recovery using polynomial models,” Proc. SPIE 5667, 65–75 (2005).

N. L. Everdell, I. B. Styles, E. Claridge, J. C. Hebden, and A. S. Calcagni, “Multispectral imaging of the ocular fundus using LED illumination,” Proc. SPIE 7371, 73711C (2009).

Other (5)

R. S. Berns and L. A. Taplin, “Practical Spectral Imaging Using a Color-Filter Array Digital Camera,” (Final Report, 2006), http://art-si.org/PDFs/Acquisition/TR_Practical_Spectral_Imaging.pdf .

S. Han, I. Sato, T. Okabe, and Y. Sato, “Fast spectral reflectance recovery using DLP projector,” in Asian Conference on Computer Vision (2010), pp. 323–335.

S. Helling, E. Seidel, and W. Biehlig, “Algorithms for spectral color stimulus reconstruction with a seven-channel multispectral camera,” in Conference on Color in Graphics, Imaging and Vision (2004), pp. 254–258.

C. Li, G. Cui, and M. R. Luo, “The accuracy of polynomial models for characterizing digital cameras,” in Proceedings of AIC2003 Bangkok: Color Communication and Management (2003), pp. 166–170.

H. Li, Z. Wu, L. Zhang, and J. Parkkinen, “SR-LLA: A novel spectral reconstruction method based on locally linear approximation,” inProceedings of IEEE Conference on Image Processing (IEEE, 2013), pp. 2029–2033.

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Figures (11)

Fig. 1
Fig. 1 Flowchart of the proposed spectral reconstruction method based on camera responses.
Fig. 2
Fig. 2 (a) Spectral sensitivities used for the simulated camera, and (b) spectral power distribution of light source.
Fig. 3
Fig. 3 (a) The CIE DE2000 color difference map of the simulated experiment in different number of responses expansion items and local optimal training samples, (b) the relationship between CIE DE2000 color difference and different number of local optimal training samples with 20 expansion items, and (c) the relationship between CIE DE2000 color difference and different number of responses expansion items with 200 local optimal training samples.
Fig. 4
Fig. 4 Boxplot of simulated experiment results: (a) CIE DE2000 color difference, (b) CIE DE76 color difference, (c) spectral RMS error, and (d) spectral GFC.
Fig. 5
Fig. 5 Comparison of simulated spectral reflectance reconstruction between our method and existing methods with three randomly selected samples: (a) 981, (b) 669, and (c) 73.
Fig. 6
Fig. 6 (a) The distribution of the training and testing samples in CIE1931-xy chromaticity diagram, and (b) spectral power distribution of the flat fluorescent lamp.
Fig. 7
Fig. 7 CIE DE2000 color difference map of the practical experiment in different number of responses expansion items and local optimal training samples.
Fig. 8
Fig. 8 Boxplot of practical experiment results: (a) CIE DE2000 color difference, (b) CIE DE76 color difference, (c) spectral RMS error, and (d) spectral GFC.
Fig. 9
Fig. 9 Comparison of practical spectral reflectance reconstruction between our method and existing methods with three randomly selected samples: (a) 58, (b) 404, and (c) 576.
Fig. 10
Fig. 10 (a) Boxplot of CIE DE2000 color difference, and (b) spectral RMS error for the simulated experiment.
Fig. 11
Fig. 11 (a) Boxplot of CIE DE2000 color difference, and (b) spectral RMS error for the practical experiment

Tables (4)

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Table 1 Comparison of simulated spectral reconstruction accuracy between our and the existing methods

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Table 2 Comparison of practical spectral reconstruction accuracy between our and the existing methods

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Table 3 Simulated spectral reconstruction accuracy of the optimized method of Zhang and Xiao

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Table 4 Practical spectral reconstruction accuracy of the optimized method of Zhang and Xiao

Equations (23)

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d i = λ min λ max l(λ) s i (λ)o(λ)r(λ)dλ+ n i .
d=Mr.
r ˜ =Qd.
r ˜ = M + d.
D Τ =T P Τ +E .
R Τ =U V Τ +F .
U=TB .
Q Τ =G ( P Τ G) 1 ( T Τ T) 1 T Τ R Τ .
C(w)= d 1 k w j d j 2 .
r ˜ = 1 k w j r j .
r ˜ =k( r 1 /Δ E 1 2 + r 2 /Δ E 2 2 + r n /Δ E n 2 ), k=1/(1/Δ E 1 2 +1/Δ E 2 2 +1/Δ E n 2 ) .
t= i=0 e j=0 e u=0 e a l r i g j b u .
r ˜ =Qt .
Q= R train T train + .
β= i=0 e j=0 e u=0 e a l r i g j b u .
r ˜ = U k β .
s j = ( r test r train, j ) 2 + ( g test g train, j ) 2 + ( b test b train, j ) 2 (j=1, 2,,N) .
w k = 1 s k +ε (k=1, 2,,p) .
W= [ w 1 0 0 0 w 2 0 0 0 0 0 w p ] p×p .
d=[1 r g b rg rb gb r 2 g 2 b 2 r g 2 r 2 g r b 2 r 2 b g b 2 g 2 b r 3 g 3 b 3 rgb r g 3 r 2 g 2 r g 3 r b 3 r 2 b 2 r 3 b g b 3 g 2 b 2 r 3 b r 2 gb r g 2 b rg b 2 r 4 g 4 b 4 ] .
Q=W R localtrain (W D localtrain ) + .
r ˜ test =Q d test .
RMS= 1 n ( r ˜ r) T ( r ˜ r) , GFC= r ˜ T r r ˜ T r ˜ r T r .

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