Towards Highlight Based Illuminant Estimation in Multispectral Images

  • Haris Ahmad KhanEmail author
  • Jean-Baptiste Thomas
  • Jon Yngve Hardeberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)


We review the physics based illuminant estimation methods, which extract information from highlights in images. Such highlights are caused by specular reflection from the surface of dielectric materials, and according to the dichromatic reflection model, provide cues about the illumination. This paper analyzes different categories of highlight based illuminant estimation techniques for color images from the point of view of their extension to multispectral imaging. We find that the use of chromaticity space for multispectral imaging is not straightforward and imposing constraints on illuminants in the multispectral imaging domain may not be efficient either. We identify some methods that are feasible for extension to multispectral imaging, and discuss the advantage of using highlight information for illuminant estimation.


Computational color constancy Illuminant estimation Dichromatic model Highlights 


  1. 1.
    Finlayson, G.D., Hordley, S.D., Hubel, P.M.: Color by correlation: a simple, unifying framework for color constancy. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1209–1221 (2001)CrossRefGoogle Scholar
  2. 2.
    Lee, H.C., Breneman, E.J., Schulte, C.P.: Modeling light reflection for computer color vision. IEEE Trans. Pattern Anal. Mach. Intell. 12, 402–409 (1990)CrossRefGoogle Scholar
  3. 3.
    Shafer, S.A.: Using color to separate reflection components. Color Res. Appl. 10(4), 210–218 (1985)CrossRefGoogle Scholar
  4. 4.
    Klinker, G.J., Shafer, S.A., Kanade, T.: The measurement of highlights in color images. Int. J. Comput. Vis. 2, 7–32 (1988)CrossRefGoogle Scholar
  5. 5.
    Lin, S., Shum, H.-Y.: Separation of diffuse and specular reflection in color images. In: Conference on Computer Vision and Pattern Recognition, pp. 341–346 (2001)Google Scholar
  6. 6.
    Lapray, P.-J., Wang, X., Thomas, J.-B., Gouton, P.: Multispectral filter arrays: recent advances and practical implementation. Sensors 14(11), 21626–21659 (2014)CrossRefGoogle Scholar
  7. 7.
    Khan, H.A., Thomas, J.B., Hardeberg, J.Y.: Multispectral constancy based on spectral adaptation transform. In: Sharma, P., Bianchi, F.M. (eds.) SCIA 2017. LNCS, vol. 10270, pp. 459–470. Springer, Cham (2017). Scholar
  8. 8.
    Khan, H.A., Thomas, J.-B., Hardeberg, J.Y., Laligant, O.: Spectral adaptation transform for multispectral constancy. J. Imaging Sci. Technol. 62(2), 20504-1–20504-12 (2018)CrossRefGoogle Scholar
  9. 9.
    Lee, H.-C.: Method for computing the scene-illuminant chromaticity from specular highlights. J. Opt. Soc. Am. A 3, 1694–1699 (1986)CrossRefGoogle Scholar
  10. 10.
    D’Zmura, M., Lennie, P.: Mechanisms of color constancy. J. Opt. Soc. Am. A 3, 1662–1672 (1986)CrossRefGoogle Scholar
  11. 11.
    Lehmann, T.M., Palm, C.: Color line search for illuminant estimation in real-world scenes. J. Opt. Soc. Am. A 18, 2679–2691 (2001)CrossRefGoogle Scholar
  12. 12.
    Uchimi, Y., Jinno, T., Kuriyama, S.: Estimation of multiple illuminant colors using color lines of single image. In: International Conference on Advanced Informatics, Concepts, Theory, and Applications, pp. 1–6, August 2017Google Scholar
  13. 13.
    Kim, J.-Y., Seo, Y.-S., Ha, Y.-H.: Estimation of illuminant chromaticity from single color image using perceived illumination and highlight. J. Imaging Sci. Technol. 45(3), 274–282 (2001)Google Scholar
  14. 14.
    Kwon, O.-S., Cho, Y.-H., Kim, Y.-T., Ha, Y.-H.: Illumination estimation based on valid pixel selection in highlight region. In: IEEE International Conference on Image Processing, October 2004Google Scholar
  15. 15.
    Lakehal, E., Ziou, D.: Computational color constancy from maximal projections mean assumption. Multimedia Tools Appl. (2017).
  16. 16.
    Finlayson, G.D., Schaefer, G.: Solving for colour constancy using a constrained dichromatic reflection model. Int. J. Comput. Vision 42, 127–144 (2001)CrossRefGoogle Scholar
  17. 17.
    Schaefer, G.: Robust dichromatic colour constancy. In: Campilho, A., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 257–264. Springer, Heidelberg (2004). Scholar
  18. 18.
    Li, Y.-Y., Lee, H.-C.: Auto white balance by surface reflection decomposition. J. Opt. Soc. Am. A 34, 1800–1809 (2017)CrossRefGoogle Scholar
  19. 19.
    Mazin, B., Delon, J., Gousseau, Y.: Illuminant estimation from projections on the planckian locus. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012. LNCS, vol. 7584, pp. 370–379. Springer, Heidelberg (2012). Scholar
  20. 20.
    Toro, J., Funt, B.: A multilinear constraint on dichromatic planes for illumination estimation. IEEE Trans. Image Process. 16, 92–97 (2007)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Toro, J.: Dichromatic illumination estimation without pre-segmentation. Pattern Recogn. Lett. 29(7), 871–877 (2008)CrossRefGoogle Scholar
  22. 22.
    Shi, L., Funt, B.: Dichromatic illumination estimation via Hough transforms in 3D. In: Conference on Color in Graphics, Imaging, & Vision, pp. 259–262 (2008)Google Scholar
  23. 23.
    Ebner, M., Herrmann, C.: On determining the color of the illuminant using the dichromatic reflection model. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 1–8. Springer, Heidelberg (2005). Scholar
  24. 24.
    Tan, R.T., Nishino, K., Ikeuchi, K.: Color constancy through inverse-intensity chromaticity space. J. Opt. Soc. Am. A 21, 321–334 (2004)CrossRefGoogle Scholar
  25. 25.
    Khan, H.A., Thomas, J.-B., Hardeberg, J.Y.: Analytical survey of highlight detection in color and spectral images. In: Bianco, S., Schettini, R., Trémeau, A., Tominaga, S. (eds.) CCIW 2017. LNCS, vol. 10213, pp. 197–208. Springer, Cham (2017). Scholar
  26. 26.
    Riess, C., Eibenberger, E., Angelopoulou, E.: Illuminant color estimation for real-world mixed-illuminant scenes. In: International Conference on Computer Vision, pp. 782–789, November 2011Google Scholar
  27. 27.
    Hara, K., Nishino, K.: Variational estimation of inhomogeneous specular reflectance and illumination from a single view. J. Opt. Soc. Am. A 28, 136–146 (2011)CrossRefGoogle Scholar
  28. 28.
    Badawi, W.K.M., Chibelushi, C.C., Patwary, M.N., Moniri, M.: Specular-based illumination estimation using blind signal separation techniques. IET Image Process. 6, 1181–1191 (2012)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Khan, H.A., Thomas, J.-B., Hardeberg, J.Y., Laligant, O.: Illuminant estimation in multispectral imaging. J. Opt. Soc. Am. A 34, 1085–1098 (2017)CrossRefGoogle Scholar
  30. 30.
    Gijsenij, A., Gevers, T., van de Weijer, J.: Improving color constancy by photometric edge weighting. IEEE Trans. Pattern Anal. Mach. Intell. 34, 918–929 (2012)CrossRefGoogle Scholar
  31. 31.
    van de Weijer, J., Gevers, T., Gijsenij, A.: Edge-based color constancy. IEEE Trans. Image Process. 16, 2207–2214 (2007)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Drew, M.S., Joze, H.R.V., Finlayson, G.D.: The zeta-image, illuminant estimation, and specularity manipulation. Comput. Vis. Image Underst. 127, 1–13 (2014)CrossRefGoogle Scholar
  33. 33.
    An, D., Suo, J., Wang, H., Dai, Q.: Illumination estimation from specular highlight in a multispectral image. Opt. Express 23, 17008–17023 (2015)CrossRefGoogle Scholar
  34. 34.
    Zheng, Y., Sato, I., Sato, Y.: Illumination and reflectance spectra separation of a hyperspectral image meets low-rank matrix factorization. In: Conference on Computer Vision and Pattern Recognition, pp. 1779–1787, June 2015Google Scholar
  35. 35.
    Chen, X., Drew, M.S., Li, Z.-N.: Illumination and reflectance spectra separation of hyperspectral image data under multiple illumination conditions. Electron. Imaging 18, 194–199 (2017)CrossRefGoogle Scholar
  36. 36.
    Tominaga, S., Wandell, B.A.: Standard surface-reflectance model and illuminant estimation. J. Opt. Soc. Am. A 6, 576–584 (1989)CrossRefGoogle Scholar
  37. 37.
    Huynh, C.P., Robles-Kelly, A.: A solution of the dichromatic model for multispectral photometric invariance. Int. J. Comput. Vis. 90, 1–27 (2010)CrossRefGoogle Scholar
  38. 38.
    Imai, Y., Kato, Y., Kadoi, H., Horiuchi, T., Tominaga, S.: Estimation of multiple illuminants based on specular highlight detection. In: Schettini, R., Tominaga, S., Trémeau, A. (eds.) CCIW 2011. LNCS, vol. 6626, pp. 85–98. Springer, Heidelberg (2011). Scholar
  39. 39.
    Hang, N.T.D., Horiuchi, T., Hirai, K., Tominaga, S.: Estimation of two illuminant spectral power distributions from highlights of overlapping illuminants. In: Signal-Image Technology & Internet-Based Systems, pp. 434–440, December 2013Google Scholar
  40. 40.
    Kato, Y., Horiuchi, T., Tominaga, S.: Estimation of multiple light sources from specular highlights. In: International Conference on Pattern Recognition, pp. 2033–2086, November 2012Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Haris Ahmad Khan
    • 1
    • 2
    Email author
  • Jean-Baptiste Thomas
    • 1
    • 2
  • Jon Yngve Hardeberg
    • 1
  1. 1.The Norwegian Colour and Visual Computing LaboratoryNTNU - Norwegian University of Science and TechnologyGjøvikNorway
  2. 2.Le2i, FRE CNRS 2005Univ. Bourgogne Franche-ComtéDijonFrance

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