CFA Based Simultaneous Multispectral Imaging and Illuminant Estimation

  • Raju Shrestha
  • Jon Yngve Hardeberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7786)

Abstract

This paper proposes an extension to the CFA based multispectral imaging with an added capability of illuminant estimation. A special filter is used on top of regular R, G and B filters of a camera, replacing one of the two green filters, with one of them. This gives a six channel multispectral image. A normal RGB image is produced by the RGB filters. The corresponding filtered RGB image is obtained using the filtered RGB channels. The two images of a scene allow estimating the illuminant using the chromagenic illuminant estimation algorithm. The proposed system is thus capable of acquiring not only multispectral image but also normal RGB image, and at the same time capable of estimating the illuminant under which the image is captured. This makes the system useful in many applications in color imaging and computer vision. Simulation experiments confirm the effectiveness of the proposed system.

Keywords

multispectral color constancy illuminant estimation chromagenic color filter array cfa mcfa 

References

  1. 1.
    Arend, L.E., Reeves, A., Schirillo, J., Goldstein, R.: Simultaneous color constancy: papers with diverse Munsell values. J. Opt. Soc. Am. A 8(4), 661–672 (1991)CrossRefGoogle Scholar
  2. 2.
    Baone, G.A., Qi, H.: Demosaicking methods for multispectral cameras using mosaic focal plane array technology. In: Spectral Imaging: Eighth International Symposium on Multispectral Color Science. SPIE Proceedings, vol. 6062, pp. 75–87 (2000)Google Scholar
  3. 3.
    Barnard, K., Cardei, V.C., Funt, B.: A comparison of computational color constancy algorithms. i: Methodology and experiments with synthesized data. IEEE Transactions on Image Processing 11(9), 972–984 (2002)CrossRefGoogle Scholar
  4. 4.
    Bayer, B.E.: Color imaging array (July 1976)Google Scholar
  5. 5.
    Brainard, D.H., Kraft, J.M., Longere, P.: Color constancy: developing empirical tests of computational models. In: Mausfeld, R., Heyer, D. (eds.) Colour Perception: From Light to Object, pp. 307–334. Oxford University Press (2003)Google Scholar
  6. 6.
    Brauers, J., Aach, T.: A color filter array based multispectral camera. In: Group, G.C. (ed.) 12. Workshop Farbbildverarbeitung. Ilmenau (October 2006)Google Scholar
  7. 7.
    Buchsbaum, G.: A spatial processor model for object colour perception. Journal of the Franklin Institute 310(1), 1–26 (1980)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Cardei, V.C., Funt, B., Barnard, K.: Estimating the scene illumination chromaticity by using a neural network. J. Opt. Soc. Am. A 19(12), 2374–2386 (2002)CrossRefGoogle Scholar
  9. 9.
    Connah, D.R., Hardeberg, J.Y.: Spectral recovery using polynomial models. In: Color Imaging X: Processing, Hardcopy, and Applications. SPIE Proceedings, vol. 5667, pp. 65–75 (2005)Google Scholar
  10. 10.
    Farrell, J.E., Sherman, D., Wandell, B.: How to turn your scanner into a colorimeter. In: Tenth International Congress on Advances in Non-Impact Printing Technologies, pp. 579–581 (1994)Google Scholar
  11. 11.
    Finlayson, G.D., Hordley, S.D., HubeL, P.M.: Color by correlation: a simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1209–1221 (2001)CrossRefGoogle Scholar
  12. 12.
    Finlayson, G.D., Hordley, S.D., Morovic, P.: Chromagenic colour constancy. In: 10th Congress of the International Colour Association (AIC), Granada, Spain, pp. 8–13 (May 2005)Google Scholar
  13. 13.
    Finlayson, G.D., Hordley, S.D., Morovic, P.: Chromagenic filter design. In: 10th Congress of the International Colour Association (AIC), Granada, Spain, pp. 1079–1083 (May 2005)Google Scholar
  14. 14.
    Forsyth, D.A.: A novel algorithm for color constancy. Int. J. Comput. Vision 5, 5–36 (1990)CrossRefGoogle Scholar
  15. 15.
    Fredembach, C., Finlayson, G.D.: The bright-chromagenic algorithm for illuminant estimation. Journal of Imaging Science and Technology 52(4), 040906-1–040908-11 (2008)Google Scholar
  16. 16.
    Gehler, P.V., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, USA, pp. 1–8 (June 2008)Google Scholar
  17. 17.
    Hardeberg, J.Y., Schmitt, F., Brettel, H.: Multispectral color image capture using a liquid crystal tunable filter. Optical Engineering 41(10), 2532–2548 (2002)CrossRefGoogle Scholar
  18. 18.
    Hardeberg, J.Y.: Filter selection for multispectral color image acquisition. Journal of Imaging Science and Technology 48(2), 105–110 (2004)Google Scholar
  19. 19.
    Hordley, S.D., Finlayson, G.D.: Reevaluation of color constancy algorithm performance. J. Opt. Soc. Am. A 23(5), 1008–1020 (2006)CrossRefGoogle Scholar
  20. 20.
    Huang, H.H.: Acquisition of multispectral images using digital cameras. In: Asian Association on Remote Sensing (ACRS) (2004)Google Scholar
  21. 21.
    Imai, F.H., Berns, R.S.: Spectral estimation using trichromatic digital cameras. In: International Symposium on Multispectral Imaging and Color Reproduction for Digital Archives, pp. 42–49 (1999)Google Scholar
  22. 22.
    Land, E.H.: The retinex theory of color vision. Scientific American 237(6), 108–128 (1977)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Lu, Y.M., Fredembach, C., Vetterli, M., Susstrunk, S.: Designing color filter arrays for the joint capture of visible and near-infrared images. In: IEEE Proceedings of the International Conference on Image Processing (2009)Google Scholar
  24. 24.
    Mansouri, A.M., Marzani, F.S., Gouton, P.: Neural networks in two cascade algorithms for spectral reflectance reconstruction. In: IEEE International Conference on Image Processing, pp. 2053–2056 (2005)Google Scholar
  25. 25.
    Miao, L., Qi, H.: A generic method for generating multi-spectral filter array. In: IEEE Proceedings of the International Conference on Image Processing, pp. 3343–3346 (2004)Google Scholar
  26. 26.
    Miao, L., Qi, H., Ramanath, R., Snyder, W.: Binary tree-based generic demosaicking algorithm for multispectral filter arrays. IEEE Transactions on Image Processing 15(11), 3550–3558 (2006)CrossRefGoogle Scholar
  27. 27.
    Nascimento, S.M.C., Ferreira, F.P., Foster, D.H.: Statistics of spatial cone-excitation ratios in natural scenes. J. Opt. Soc. Am. A 19(8), 1484–1490 (2002)CrossRefGoogle Scholar
  28. 28.
    Omega: Omega filters. Omega Optical, Inc., https://www.omegafilters.com/Products/Curvomatic (Last Visited: November 2012)
  29. 29.
    Shrestha, R., Hardeberg, J.Y., Mansouri, A.: One-shot multispectral color imaging with a stereo camera. In: Imai, F.H., Xiao, F. (eds.) Digital Photography VII, Electronic Imaging. Proceedings of SPIE/IS&T Electronic Imaging, vol. 7876, pp. 787609-787609-11. SPIE, San Francisco (2011)Google Scholar
  30. 30.
    Shrestha, R., Hardeberg, J.Y.: Computaional color constancy using a stereo camera. In: 6th European Conference on Color in Graphics, Image and Vision (CGIV). IS&T, Amsterdam (May 2012)Google Scholar
  31. 31.
    Shrestha, R., Hardeberg, J.Y.: Computational color constancy using chromagenic filters in color filter arrays. In: Widenhorn, R., Nguyen, V., Dupret, A. (eds.) Sensors, Cameras, and Systems for Industrial/Scientific Applications XIII. Proceedings of SPIE/IS&T Electronic Imaging, vol. 8298, pp. 82980S–82980S-9. SPIE, San Francisco (2012)Google Scholar
  32. 32.
    Shrestha, R., Hardeberg, J.Y.: Simultaneous Multispectral Imaging and Illuminant Estimation Using a Stereo Camera. In: Elmoataz, A., Mammass, D., Lezoray, O., Nouboud, F., Aboutajdine, D. (eds.) ICISP 2012. LNCS, vol. 7340, pp. 45–55. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  33. 33.
    Shrestha, R., Hardeberg, J.Y., Khan, R.: Spatial arrangement of color filter array for multispectral image acquisition. In: Widenhorn, R., Nguyen, V., Dupret, A. (eds.) Sensors, Cameras, and Systems for Industrial, Scientific, and Consumer Applications XII, Electronic Imaging. Proceedings of SPIE/IS&T Electronic Imaging, vol. 7875, pp. 787503–787503-9. SPIE, San Francisco (2011)Google Scholar
  34. 34.
    Shrestha, R., Mansouri, A., Hardeberg, J.Y.: Multispectral imaging using a stereo camera: Concept, design and assessment. EURASIP Journal on Advances in Signal Processing 2011(1) (September 2011)Google Scholar
  35. 35.
    Tominaga, S.: Spectral imaging by a multichannel camera. Journal of Electronic Imaging 8(4), 332–341 (1999)CrossRefGoogle Scholar
  36. 36.
    Yamaguchi, M., Haneishi, H., Ohyama, N.: Beyond Red–Green–Blue (RGB): Spectrum-based color imaging technology. Journal of Imaging Science and Technology 52(1), 10201-1–10201-15 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Raju Shrestha
    • 1
  • Jon Yngve Hardeberg
    • 1
  1. 1.The Norwegian Colour and Visual Computing LaboratoryGjøvik University CollegeGjøvikNorway

Personalised recommendations