Abstract

With the advancement in sensor technology, the use of multispectral imaging is gaining wide popularity for computer vision applications. Multispectral imaging is used to achieve better discrimination between the radiance spectra, as compared to the color images. However, it is still sensitive to illumination changes. This study evaluates the potential evolution of illuminant estimation models from color to multispectral imaging. We first present a state of the art on computational color constancy and then extend a set of algorithms to use them in multispectral imaging. We investigate the influence of camera spectral sensitivities and the number of channels. Experiments are performed on simulations over hyperspectral data. The outcomes indicate that extension of computational color constancy algorithms from color to spectral gives promising results and may have the potential to lead towards efficient and stable representation across illuminants. However, this is highly dependent on spectral sensitivities and noise. We believe that the development of illuminant invariant multispectral imaging systems will be a key enabler for further use of this technology.

© 2017 Optical Society of America

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References

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2017 (2)

S. W. Oh and S. J. Kim, “Approaching the computational color constancy as a classification problem through deep learning,” Pattern Recogn. 61, 405–416 (2017).
[Crossref]

P.-J. Lapray, J.-B. Thomas, P. Gouton, and Y. Ruichek, “Energy balance in spectral filter array camera design,” J. Eur. Opt. Soc. 13, 1 (2017).
[Crossref]

2016 (2)

G. D. Finlayson, R. Zakizadeh, and A. Gijsenij, “The reproduction angular error for evaluating the performance of illuminant estimation algorithms,” IEEE Trans. Pattern Anal. Mach. Intell. PP, 1 (2016).
[Crossref]

J.-B. Thomas, P.-J. Lapray, P. Gouton, and C. Clerc, “Spectral characterization of a prototype SFA camera for joint visible and NIR acquisition,” Sensors 16, 993 (2016).
[Crossref]

2014 (5)

C. Liu, W. Liu, X. Lu, F. Ma, W. Chen, J. Yang, and L. Zheng, “Application of multispectral imaging to determine quality attributes and ripeness stage in strawberry fruit,” PLoS ONE 9, e87818 (2014).
[Crossref]

P.-J. Lapray, X. Wang, J.-B. Thomas, and P. Gouton, “Multispectral filter arrays: Recent advances and practical implementation,” Sensors 14, 21626–21659 (2014).
[Crossref]

X. Wang, J.-B. Thomas, J. Y. Hardeberg, and P. Gouton, “Multispectral imaging: narrow or wide band filters?” J. Int. Colour Assoc. 12, 44–51 (2014).

M. Rezagholizadeh and J. J. Clark, “Image sensor modeling: color measurement at low light levels,” J. Imaging Sci. Technol. 58, 304011 (2014).
[Crossref]

R. Shrestha and J. Y. Hardeberg, “Spectrogenic imaging: a novel approach to multispectral imaging in an uncontrolled environment,” Opt. Express 22, 9123–9133 (2014).
[Crossref]

2012 (2)

T. Celik and T. Tjahjadi, “Adaptive colour constancy algorithm using discrete wavelet transform,” Comput. Vis. Image Underst. 116, 561–571 (2012).
[Crossref]

A. Chakrabarti, K. Hirakawa, and T. Zickler, “Color constancy with spatio-spectral statistics,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 1509–1519 (2012).
[Crossref]

2011 (3)

A. Gijsenij and T. Gevers, “Color constancy using natural image statistics and scene semantics,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 687–698 (2011).
[Crossref]

R. Shrestha, J. Y. Hardeberg, and R. Khan, “Spatial arrangement of color filter array for multispectral image acquisition,” Proc. SPIE 7875, 787503 (2011).
[Crossref]

D. H. Brainard and L. T. Maloney, “Surface color perception and equivalent illumination models,” J. Vis. 11(5), 1 (2011).
[Crossref]

2010 (2)

A. Gijsenij, T. Gevers, and J. van de Weijer, “Generalized gamut mapping using image derivative structures for color constancy,” Int. J. Comput. Vis. 86, 127–139 (2010).
[Crossref]

S. Ratnasingam and S. Collins, “Study of the photodetector characteristics of a camera for color constancy in natural scenes,” J. Opt. Soc. Am. A 27, 286–294 (2010).
[Crossref]

2009 (1)

N. Wang, D. Xu, and B. Li, “Edge-based color constancy via support vector regression,” IEICE Trans. Inf. Syst. E92-D, 2279–2282 (2009).
[Crossref]

2008 (3)

C. Fredembach and G. Finlayson, “Bright-chromagenic algorithm for illuminant estimation,” J. Imaging Sci. Technol. 52, 040906 (2008).
[Crossref]

J. L. Nieves, C. Plata, E. M. Valero, and J. Romero, “Unsupervised illuminant estimation from natural scenes: an RGB digital camera suffices,” Appl. Opt. 47, 3574–3584 (2008).
[Crossref]

S. Bianco, F. Gasparini, and R. Schettini, “Consensus-based framework for illuminant chromaticity estimation,” J. Electron. Imaging 17, 023013 (2008).
[Crossref]

2007 (4)

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

J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Process. 16, 2207–2214 (2007).
[Crossref]

V. Agarwal, A. V. Gribok, and M. A. Abidi, “Machine learning approach to color constancy,” Neural Netw. 20, 559–563 (2007).
[Crossref]

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 and colored filters,” Color Res. Appl. 32, 352–360 (2007).
[Crossref]

2006 (4)

S. D. Hordley, “Scene illuminant estimation: past, present, and future,” Color Res. Appl. 31, 303–314 (2006).
[Crossref]

J. Huo, Y. Chang, J. Wang, and X. Wei, “Robust automatic white balance algorithm using gray color points in images,” IEEE Trans. Consum. Electron. 52, 541–546 (2006).
[Crossref]

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

D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A 23, 2359–2372 (2006).
[Crossref]

2004 (2)

J. Conde, H. Haneishi, M. Yamaguchi, N. Ohyama, and J. Baez, “CIE-XYZ fitting by multispectral images and mean square error minimization with a linear interpolation function,” Rev. Mex. Fis. 6, 601–607 (2004).

K.-S. Lee, W. B. Cohen, R. E. Kennedy, T. K. Maiersperger, and S. T. Gower, “Hyperspectral versus multispectral data for estimating leaf area index in four different biomes,” Remote Sens. Environ. 91, 508–520 (2004).
[Crossref]

2002 (3)

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
[Crossref]

K. Barnard and B. Funt, “Camera characterization for color research,” Color Res. Appl. 27, 152–163 (2002).
[Crossref]

V. C. Cardei, B. Funt, and K. Barnard, “Estimating the scene illumination chromaticity by using a neural network,” J. Opt. Soc. Am. A 19, 2374–2386 (2002).
[Crossref]

2001 (3)

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

D. Connah, S. Westland, and M. G. A. Thomson, “Recovering spectral information using digital camera systems,” Coloration Technol. 117, 309–312 (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. Mach. Intell. 23, 1209–1221 (2001).
[Crossref]

1999 (3)

O. Bertr and C. Tallon-Baudry, “Oscillatory gamma activity in humans: a possible role for object representation,” Trends Cogn. Sci. 3, 151–162 (1999).
[Crossref]

G. Sapiro, “Color and illuminant voting,” IEEE Trans. Pattern Anal. Mach. Intell. 21, 1210–1215 (1999).
[Crossref]

T. Gevers and A. W. Smeulders, “Color-based object recognition,” Pattern Recogn. 32, 453–464 (1999).
[Crossref]

1997 (3)

1995 (1)

B. V. Funt and G. D. Finlayson, “Color constant color indexing,” IEEE Trans. Pattern Anal. Mach. Intell. 17, 522–529 (1995).
[Crossref]

1994 (1)

1993 (1)

1991 (1)

M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis. 7, 11–32 (1991).
[Crossref]

1990 (1)

D. Forsyth, “A novel algorithm for color constancy,” Int. J. Comput. Vis. 5, 5–35 (1990).
[Crossref]

1987 (1)

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

1986 (2)

1983 (1)

E. Land, “Recent advances in retinex theory and some implications for cortical computations: color vision and the natural image,” Proc. Natl. Acad. Sci. USA 80, 5163–5169 (1983).
[Crossref]

1980 (1)

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

1977 (1)

E. H. Land, “The retinex theory of color vision,” Sci. Am. 237, 108–128 (1977).
[Crossref]

1971 (1)

Abidi, M. A.

V. Agarwal, A. V. Gribok, and M. A. Abidi, “Machine learning approach to color constancy,” Neural Netw. 20, 559–563 (2007).
[Crossref]

Agarwal, V.

V. Agarwal, A. V. Gribok, and M. A. Abidi, “Machine learning approach to color constancy,” Neural Netw. 20, 559–563 (2007).
[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 and colored filters,” Color Res. Appl. 32, 352–360 (2007).
[Crossref]

D. H. Foster, K. Amano, S. M. C. Nascimento, and M. J. Foster, “Frequency of metamerism in natural scenes,” J. Opt. Soc. Am. A 23, 2359–2372 (2006).
[Crossref]

Baez, J.

J. Conde, H. Haneishi, M. Yamaguchi, N. Ohyama, and J. Baez, “CIE-XYZ fitting by multispectral images and mean square error minimization with a linear interpolation function,” Rev. Mex. Fis. 6, 601–607 (2004).

Ballard, D. H.

M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. Comput. Vis. 7, 11–32 (1991).
[Crossref]

Barnard, K.

K. Barnard, V. Cardei, and B. Funt, “A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data,” IEEE Trans. Image Process. 11, 972–984 (2002).
[Crossref]

K. Barnard and B. Funt, “Camera characterization for color research,” Color Res. Appl. 27, 152–163 (2002).
[Crossref]

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G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in Color and Imaging Conference, Scottsdale, Arizona, 2004, Vol. 1, pp. 37–41.

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Supplementary Material (6)

NameDescription
» Data File 1: CSV (10 KB)      Results with D65, without noise.
» Data File 2: CSV (11 KB)      Results with D65, including noise.
» Data File 3: CSV (10 KB)      Results with F11, without noise.
» Data File 4: CSV (10 KB)      Results with F11, including noise.
» Data File 5: CSV (10 KB)      Results with mixed D65-F11, without noise.
» Data File 6: CSV (10 KB)      Results with mixed D65-F11, including noise.

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Figures (9)

Fig. 1.
Fig. 1.

Rendering of hyperspectral images from Foster Dataset 2004 into RGB with D65 illuminant. The hyperspectral images are acquired within the wavelength range of 400–720 nm with 10 nm sampling. Each hyperspectral image consists of 33 spectral bands.

Fig. 2.
Fig. 2.

Illuminants used for creating radiance data from hyperspectral reflectance data are shown. Normalization is performed by diving each value by the maximum of that illuminant so that all values are within the range of [0–1]. (a) D65 Illuminant. (b) F11 Illuminant. In (c), the mix D65-F11 illuminant consists of 50% D65 and 50% F11.

Fig. 3.
Fig. 3.

Filter configurations. For a configuration denoted S x y , x is the number of filters and y represents the configuration where we have the following: g, equi-Gaussian; d, Dirac delta; f, filter with constant FWHM. Here we show examples with eight filters. (a)  S 8 g Equi-Gaussian filters. (b)  S 8 d Dirac delta filters. (c)  S 8 50 Equi-energy filters.

Fig. 4.
Fig. 4.

Illuminant’s projection over filters (IPF) of D65 with N = 3 channels and estimated illuminants with spectral gray edge S 3 g for images I3 and I4 with Δ A of 0.228 and 0.0158, respectively. I4 gives good results, while I3 performs worst.

Fig. 5.
Fig. 5.

Illuminant’s projection over filters (IPF) of D65 with N = 5 channels and estimated illuminants with spectral gray edge S 5 50 in sensor domain for images I3 and I5 with Δ A of 0.2284 and 0.0457, respectively. I5 gives good results, while I3 performs worst.

Fig. 6.
Fig. 6.

Illuminant’s projection over filters (IPF) of D65 with N = 8 channels and estimated illuminants with max-spectral S 8 50 in sensor domain for images I3 and I5 with Δ A of 0.1142 and 0.0446, respectively. I5 gives good results, while I3 performs worst.

Fig. 7.
Fig. 7.

Illuminant’s projection over filters (IPF) of D65 with N = 12 channels and estimated illuminants with max-spectral S 12 50 in sensor domain for images I6 and I7 with Δ A of 0.0838 and 0.0117, respectively. I7 gives good results.

Fig. 8.
Fig. 8.

Illuminant’s projection over filters (IPF) of D65 with N = 20 channels and estimated illuminants with max-spectral S 20 50 in sensor domain for images I6 and I7 with Δ A of 0.0839 and 0.0117, respectively. I7 gives good results.

Fig. 9.
Fig. 9.

Change in angular error with variation in p . We did experiments for p = 1 1000 but show results only up to 300 because there is no change in error value as the value of p is increased beyond 100.

Tables (6)

Tables Icon

Table 1. Performance of Illuminant Estimation Algorithms and Filter Configurations for Three Bands a

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Table 2. Performance of Illuminant Estimation Algorithms and Filter Configurations for Five Bands a

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Table 3. Performance of Illuminant Estimation Algorithms and Filter Configurations for Eight Bands a

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Table 4. Performance of Illuminant Estimation Algorithms and Filter Configurations for 12 Bands a

Tables Icon

Table 5. Performance of Illuminant Estimation Algorithms and Filter Configurations for 20 Bands a

Tables Icon

Table 6. Ranking Based on x y Chromaticity Error in Terms of Euclidean Distance (ED) a

Equations (9)

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

f = ω e ( λ ) r ( λ ) c ( λ ) d λ .
e = ( R e G e B e ) = ω e ( λ ) c ( λ ) d λ .
F c = D u , c F u ,
( F p d x d x ) 1 / p = k e ,
( [ [ F σ ] p d x d x ) 1 / p = k e ,
f = ω e ( λ ) r ( λ ) m ( λ ) d λ ,
( r ( λ ) d x d x ) = k .
f ( λ ) d x d x = 1 d x ω e ( λ ) r ( λ ) m ( λ ) d λ d x = k ω e ( λ ) m ( λ ) d λ = k e ^ .
Δ A = arccos e T e ^ ( e T e ) ( e ^ T e ^ ) ,

Metrics