Abstract

A linear pseudo-inverse method for unsupervised illuminant recovery from natural scenes is presented. The algorithm, which uses a digital RGB camera, selects the naturally occurring bright areas (not necessarily the white ones) in natural images and converts the RGB digital counts directly into the spectral power distribution of the illuminants using a learning-based spectral procedure. Computations show a good spectral and colorimetric performance when only three sensors (a three-band RGB camera) are used. These results go against previous findings concerning the recovery of spectral reflectances and radiances, which claimed that the greater the number of sensors, the better the spectral performance. Combining the device with the appropriate computations can yield spectral information about objects and illuminants simultaneously, avoiding the need for spectroradiometric measurements. The method works well and needs neither a white reference located in the natural scene nor direct measurements of the spectral power distribution of the light.

© 2008 Optical Society of America

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  1. B. A. Wandell, “The synthesis and analysis of color images,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 2-13 (1987).
    [CrossRef] [PubMed]
  2. J. Y. Hardeberg, “Acquisition and reproduction of color images: colorimetric and multispectral approaches,” Ph.D. dissertation (Ecole Nationale Superieure des Telecommunications, 1999).
  3. M. Ebner, Color Constancy (Wiley, 2007).
  4. S. Tominaga, “Multichannel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163-2173 (1996).
    [CrossRef]
  5. F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.
  6. C.-C. Chiao, D. Osorio, M. Vorobyev, and T. W. Cronin, “Characterization of natural illuminants in forests and the use of digital video data to reconstruct illuminant spectra,” J. Opt. Soc. Am. A 17, 1713-11721 (2000).
    [CrossRef]
  7. S. M. C. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484-1490 (2002).
    [CrossRef]
  8. V. Cheung, S. Westland, C. Li, J. Hardeberg, and D. Connah, “Characterization of trichromatic color cameras by using a new multispectral imaging technique,” J. Opt. Soc. Am. A 22, 1231-1240 (2005).
    [CrossRef]
  9. J. L. Nieves, E. M. Valero, S. M. C. Nascimento, J. Hernández-Andrés, and J. Romero, “Multispectral synthesis of daylight using a commercial digital CCD camera,” Appl. Opt. 44, 5696-5703 (2005).
    [CrossRef] [PubMed]
  10. N. Shimano, “Evaluation of a multispectral image acquisition system aimed at reconstruction of spectral reflectances,” Opt. Eng. 44, 107005 (2005).
    [CrossRef]
  11. H.-L. Shen, J. H. Xin, and S.-J. Shao, “Improved reflectance reconstruction for multispectral imaging by combining different techniques,” Opt. Express 15, 5531-5536 (2007).
    [CrossRef] [PubMed]
  12. S. Tominaga and B. A. Wandell, “Standard surface-reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576-584 (1989).
    [CrossRef]
  13. J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, Jr., “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325-1335 (2001).
    [CrossRef]
  14. S. Tominaga, “Natural image database and its use for scene illuminant estimation,” J. Electron. Imaging 11, 434-444 (2002).
    [CrossRef]
  15. S. Tominaga and B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” in Proc. IEEE 90, 42-56 (2002).
    [CrossRef]
  16. E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera,” Color Res. Appl. 32, 352-360(2007).
    [CrossRef]
  17. J. L. Nieves, E. M. Valero, J. Hernández-Andrés, and J. Romero, “Recovering fluorescent spectra of lights with an RGB digital camera and color filters using different matrix factorizations,” Appl. Opt. 46, 4144-4154 (2007).
    [CrossRef] [PubMed]
  18. M. D'Zmura and G. Iverson, “Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148-2165(1993).
    [CrossRef]
  19. D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393-1411 (1997).
    [CrossRef]
  20. G. Shaefer and S. Hordley, “A combined physical and statistical approach to color constancy,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (IEEE Computer Society, 2005), Vol. 1, pp. 148-153.
    [CrossRef]
  21. S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
    [CrossRef]
  22. G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1209-1221(2001).
    [CrossRef]
  23. D. A. Forsyth, “A novel algorithm for colour constancy,” Int. J. Comput. Vision 5, 5-36 (1990).
    [CrossRef]
  24. S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210-218 (1985).
    [CrossRef]
  25. M. A. López-Álvarez, J. Hernández-Andrés, E. M. Valero, and J. Romero, “Selecting algorithms, sensors and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. A 24, 942-956 (2007).
    [CrossRef]
  26. M. Mosny and B. Funt, “Multispectral color constancy: real image tests” Proc. SPIE 6492, 64920S (2007).
    [CrossRef]
  27. N. Otsu, “A threshold selection method from grey-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62-66 (1979).
    [CrossRef]
  28. G. Schaefer, “Robust dichromatic colour constancy,” in Image Analysis and Recognition. Part II, A. Campilho and M. Kamel, eds. (Springer2004), pp. 257-264.
  29. G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1-26 (1980).
    [CrossRef]
  30. V. C. Cardei and B. Funt, “Committee-based color constancy,” in Proceedings of the IS&T/SID Seventh Color Imaging Conference (Society for Imaging Science and Technology, 1999), pp. 311-313.
  31. F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of the First European Conference on Colour in Graphics, Imaging and Vision (Society for Imaging Science and Technology (2002), pp. 492-496.
  32. M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4-9 (1994).

2007

2006

2005

2002

S. Tominaga, “Natural image database and its use for scene illuminant estimation,” J. Electron. Imaging 11, 434-444 (2002).
[CrossRef]

S. Tominaga and B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” in Proc. IEEE 90, 42-56 (2002).
[CrossRef]

S. M. C. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484-1490 (2002).
[CrossRef]

2001

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, Jr., “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325-1335 (2001).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1209-1221(2001).
[CrossRef]

2000

1997

1996

1994

M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4-9 (1994).

1993

1990

D. A. Forsyth, “A novel algorithm for colour constancy,” Int. J. Comput. Vision 5, 5-36 (1990).
[CrossRef]

1989

1987

B. A. Wandell, “The synthesis and analysis of color images,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 2-13 (1987).
[CrossRef] [PubMed]

1985

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210-218 (1985).
[CrossRef]

1980

G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1-26 (1980).
[CrossRef]

1979

N. Otsu, “A threshold selection method from grey-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62-66 (1979).
[CrossRef]

Amano, K.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera,” Color Res. Appl. 32, 352-360(2007).
[CrossRef]

Berns, R.

F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.

Berns, R. S.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of the First European Conference on Colour in Graphics, Imaging and Vision (Society for Imaging Science and Technology (2002), pp. 492-496.

Brainard, D. H.

Buchsbaum, G.

G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1-26 (1980).
[CrossRef]

Cardei, V. C.

V. C. Cardei and B. Funt, “Committee-based color constancy,” in Proceedings of the IS&T/SID Seventh Color Imaging Conference (Society for Imaging Science and Technology, 1999), pp. 311-313.

Cheung, V.

Chiao, C.-C.

Connah, D.

Cronin, T. W.

D'Zmura, M.

Ebner, M.

M. Ebner, Color Constancy (Wiley, 2007).

Ferreira, F. P.

Finlayson, G. D.

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1209-1221(2001).
[CrossRef]

Forsyth, D. A.

D. A. Forsyth, “A novel algorithm for colour constancy,” Int. J. Comput. Vision 5, 5-36 (1990).
[CrossRef]

Foster, D. H.

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera,” Color Res. Appl. 32, 352-360(2007).
[CrossRef]

S. M. C. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484-1490 (2002).
[CrossRef]

Freeman, W. T.

Funt, B.

M. Mosny and B. Funt, “Multispectral color constancy: real image tests” Proc. SPIE 6492, 64920S (2007).
[CrossRef]

V. C. Cardei and B. Funt, “Committee-based color constancy,” in Proceedings of the IS&T/SID Seventh Color Imaging Conference (Society for Imaging Science and Technology, 1999), pp. 311-313.

Gershon, R.

M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4-9 (1994).

Hardeberg, J.

Hardeberg, J. Y.

J. Y. Hardeberg, “Acquisition and reproduction of color images: colorimetric and multispectral approaches,” Ph.D. dissertation (Ecole Nationale Superieure des Telecommunications, 1999).

Hernández-Andrés, J.

Hordley, S.

G. Shaefer and S. Hordley, “A combined physical and statistical approach to color constancy,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (IEEE Computer Society, 2005), Vol. 1, pp. 148-153.
[CrossRef]

Hordley, S. D.

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
[CrossRef]

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1209-1221(2001).
[CrossRef]

Hubel, P. M.

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1209-1221(2001).
[CrossRef]

Imai, F. H.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of the First European Conference on Colour in Graphics, Imaging and Vision (Society for Imaging Science and Technology (2002), pp. 492-496.

F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.

Iverson, G.

Iwan, L. S.

M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4-9 (1994).

Lee, R. L.

Li, C.

López-Álvarez, M. A.

Mosny, M.

M. Mosny and B. Funt, “Multispectral color constancy: real image tests” Proc. SPIE 6492, 64920S (2007).
[CrossRef]

Nascimento, S. M. C.

Nieves, J. L.

Osorio, D.

Otsu, N.

N. Otsu, “A threshold selection method from grey-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62-66 (1979).
[CrossRef]

Romero, J.

Rosen, M. R.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of the First European Conference on Colour in Graphics, Imaging and Vision (Society for Imaging Science and Technology (2002), pp. 492-496.

Schaefer, G.

G. Schaefer, “Robust dichromatic colour constancy,” in Image Analysis and Recognition. Part II, A. Campilho and M. Kamel, eds. (Springer2004), pp. 257-264.

Shaefer, G.

G. Shaefer and S. Hordley, “A combined physical and statistical approach to color constancy,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (IEEE Computer Society, 2005), Vol. 1, pp. 148-153.
[CrossRef]

Shafer, S. A.

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210-218 (1985).
[CrossRef]

Shao, S.-J.

Shen, H.-L.

Shimano, N.

N. Shimano, “Evaluation of a multispectral image acquisition system aimed at reconstruction of spectral reflectances,” Opt. Eng. 44, 107005 (2005).
[CrossRef]

Tominaga, S.

S. Tominaga, “Natural image database and its use for scene illuminant estimation,” J. Electron. Imaging 11, 434-444 (2002).
[CrossRef]

S. Tominaga and B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” in Proc. IEEE 90, 42-56 (2002).
[CrossRef]

S. Tominaga, “Multichannel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163-2173 (1996).
[CrossRef]

S. Tominaga and B. A. Wandell, “Standard surface-reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576-584 (1989).
[CrossRef]

Valero, E. M.

Vorobyev, M.

Vrhel, M. J.

M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4-9 (1994).

Wandell, B. A.

S. Tominaga and B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” in Proc. IEEE 90, 42-56 (2002).
[CrossRef]

S. Tominaga and B. A. Wandell, “Standard surface-reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576-584 (1989).
[CrossRef]

B. A. Wandell, “The synthesis and analysis of color images,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 2-13 (1987).
[CrossRef] [PubMed]

Westland, S.

Xin, J. H.

Appl. Opt.

Color Res. Appl.

M. J. Vrhel, R. Gershon, and L. S. Iwan, “Measurement and analysis of object reflectance spectra,” Color Res. Appl. 19, 4-9 (1994).

E. M. Valero, J. L. Nieves, S. M. C. Nascimento, K. Amano, and D. H. Foster, “Recovering spectral data from natural scenes with an RGB digital camera,” Color Res. Appl. 32, 352-360(2007).
[CrossRef]

S. A. Shafer, “Using color to separate reflection components,” Color Res. Appl. 10, 210-218 (1985).
[CrossRef]

IEEE Trans. Pattern Anal. Mach. Intell.

B. A. Wandell, “The synthesis and analysis of color images,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 2-13 (1987).
[CrossRef] [PubMed]

IEEE Trans. Pattern Anal. Machine Intell.

G. D. Finlayson, S. D. Hordley, and P. M. Hubel, “Color by correlation: a simple, unifying framework for color constancy,” IEEE Trans. Pattern Anal. Machine Intell. 23, 1209-1221(2001).
[CrossRef]

IEEE Trans. Syst. Man. Cybern.

N. Otsu, “A threshold selection method from grey-level histograms,” IEEE Trans. Syst. Man. Cybern. 9, 62-66 (1979).
[CrossRef]

Int. J. Comput. Vision

D. A. Forsyth, “A novel algorithm for colour constancy,” Int. J. Comput. Vision 5, 5-36 (1990).
[CrossRef]

J. Electron. Imaging

S. Tominaga, “Natural image database and its use for scene illuminant estimation,” J. Electron. Imaging 11, 434-444 (2002).
[CrossRef]

J. Franklin Inst.

G. Buchsbaum, “A spatial processor model for object color perception,” J. Franklin Inst. 310, 1-26 (1980).
[CrossRef]

J. Opt. Soc. Am. A

D. H. Brainard and W. T. Freeman, “Bayesian color constancy,” J. Opt. Soc. Am. A 14, 1393-1411 (1997).
[CrossRef]

S. Tominaga and B. A. Wandell, “Standard surface-reflectance model and illuminant estimation,” J. Opt. Soc. Am. A 6, 576-584 (1989).
[CrossRef]

M. D'Zmura and G. Iverson, “Color constancy. I. Basic theory of two-stage linear recovery of spectral descriptions for lights and surfaces,” J. Opt. Soc. Am. A 10, 2148-2165(1993).
[CrossRef]

S. Tominaga, “Multichannel vision system for estimating surface and illumination functions,” J. Opt. Soc. Am. A 13, 2163-2173 (1996).
[CrossRef]

C.-C. Chiao, D. Osorio, M. Vorobyev, and T. W. Cronin, “Characterization of natural illuminants in forests and the use of digital video data to reconstruct illuminant spectra,” J. Opt. Soc. Am. A 17, 1713-11721 (2000).
[CrossRef]

J. Hernández-Andrés, J. Romero, J. L. Nieves, and R. L. Lee, Jr., “Color and spectral analysis of daylight in southern Europe,” J. Opt. Soc. Am. A 18, 1325-1335 (2001).
[CrossRef]

S. M. C. Nascimento, F. P. Ferreira, and D. H. Foster, “Statistics of spatial cone excitation ratios in natural scenes,” J. Opt. Soc. Am. A 19, 1484-1490 (2002).
[CrossRef]

V. Cheung, S. Westland, C. Li, J. Hardeberg, and D. Connah, “Characterization of trichromatic color cameras by using a new multispectral imaging technique,” J. Opt. Soc. Am. A 22, 1231-1240 (2005).
[CrossRef]

S. D. Hordley and G. D. Finlayson, “Reevaluation of color constancy algorithm performance,” J. Opt. Soc. Am. A 23, 1008-1020 (2006).
[CrossRef]

M. A. López-Álvarez, J. Hernández-Andrés, E. M. Valero, and J. Romero, “Selecting algorithms, sensors and linear bases for optimum spectral recovery of skylight,” J. Opt. Soc. Am. A 24, 942-956 (2007).
[CrossRef]

Opt. Eng.

N. Shimano, “Evaluation of a multispectral image acquisition system aimed at reconstruction of spectral reflectances,” Opt. Eng. 44, 107005 (2005).
[CrossRef]

Opt. Express

Proc. IEEE

S. Tominaga and B. A. Wandell, “Natural scene-illuminant estimation using the sensor correlation,” in Proc. IEEE 90, 42-56 (2002).
[CrossRef]

Proc. SPIE

M. Mosny and B. Funt, “Multispectral color constancy: real image tests” Proc. SPIE 6492, 64920S (2007).
[CrossRef]

Other

V. C. Cardei and B. Funt, “Committee-based color constancy,” in Proceedings of the IS&T/SID Seventh Color Imaging Conference (Society for Imaging Science and Technology, 1999), pp. 311-313.

F. H. Imai, M. R. Rosen, and R. S. Berns, “Comparative study of metrics for spectral match quality,” in Proceedings of the First European Conference on Colour in Graphics, Imaging and Vision (Society for Imaging Science and Technology (2002), pp. 492-496.

G. Schaefer, “Robust dichromatic colour constancy,” in Image Analysis and Recognition. Part II, A. Campilho and M. Kamel, eds. (Springer2004), pp. 257-264.

G. Shaefer and S. Hordley, “A combined physical and statistical approach to color constancy,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (IEEE Computer Society, 2005), Vol. 1, pp. 148-153.
[CrossRef]

J. Y. Hardeberg, “Acquisition and reproduction of color images: colorimetric and multispectral approaches,” Ph.D. dissertation (Ecole Nationale Superieure des Telecommunications, 1999).

M. Ebner, Color Constancy (Wiley, 2007).

F. H. Imai and R. Berns, “Spectral estimation using trichromatic digital cameras,” in Proceedings of the International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives (Society of Multispectral Imaging of Japan, 1999), pp. 42-49.

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Figures (6)

Fig. 1
Fig. 1

(a) Spectral sensitivities of the RGB digital camera (QImaging Retiga 1300); (b) spectral transmittance of the GG475 colored-glass filter from OWIS GmbH.

Fig. 2
Fig. 2

Examples of the spectral-illuminant recovery (left-hand panels) with k = 3 sensors and (right-hand panels) with k = 6 sensors for different illuminants and scenes (—, original spectrum; •, recovered spectrum).

Fig. 3
Fig. 3

Example of the seeded regions obtained by using different luminance threshold values. Results for the cumulative distributions of GFCs with and without a prefilter are also shown for this scene.

Fig. 4
Fig. 4

Mean spectral (left) and colorimetric (right) performance for the linear pseudo-inverse algorithm using different luminance threshold values.

Fig. 5
Fig. 5

Visualized images of original scene fragments and the histogram of RGB errors. The upper histograms are for the camera without a prefilter, and the lower ones for the six-band camera.

Fig. 6
Fig. 6

Visualized image of an indoor scene fragment and the histogram of RGB errors. Results are examples of the spectral-illuminant recovery with k = 3 sensors and two artificial illuminants (—, original spectrum; •, recovered spectrum).

Tables (2)

Tables Icon

Table 1 Linear Pseudo-Inverse Illuminant Estimation Using a 50% Luminance Threshold a

Tables Icon

Table 2 Summary of Performance (AE, degrees) for Linear Pseudo-Inverse Illuminant Estimation and Different Color-Constancy Algorithms

Equations (7)

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

ρ k x = λ = 400 700 E x ( λ ) r x ( λ ) Q k ( λ ) Δ λ ,
ρ = ρ + η = C E ,
E = F ρ .
F = E o [ ( ρ o T ρ o ) 1 ρ o T ] = E o ρ + o ,
GFC = λ = 400 700 f ( λ ) f r ( λ ) [ λ = 400 700 f ( λ ) 2 ] 1 / 2 [ λ = 400 700 f r ( λ ) 2 ] 1 / 2 .
AE = cos 1 ( ρ l · ρ e ) ,
RGBerror x = [ 1 3 i = 1 3 ( ρ i x ρ ̲ i x ) 2 ] 1 / 2 ,

Metrics