Flash Lighting Space Sampling

  • Matteo Dellepiane
  • Marco Callieri
  • Massimiliano Corsini
  • Paolo Cignoni
  • Roberto Scopigno
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5496)

Abstract

Flash light of digital cameras is a very useful way to picture scenes with low quality illumination. Nevertheless, especially low-end cameras integrated flash lights are considered as not reliable for high quality images, due to known artifacts (sharp shadows, highlights, uneven lighting) generated in images. Moreover, a mathematical model of this kind of light seems difficult to create. In this paper we present a color correction space which, given some information about the geometry of the pictured scene, is able to provide a space-dependent correction of each pixel of the image. The correction space can be calculated once in a lifetime using a quite fast acquisition procedure; after 3D spatial calibration, obtained color correction function can be applied to every image where flash is the dominant illuminant. The correction space presents several advantages: it is independent from the kind of light used (provided that it is bound to the camera), it gives the possibly to correct only determinate artifacts (for example color deviation) introduced by flash light, and it has a wide range of possible applications, from image enhancement to material color estimation.

Keywords

Color shading shadowing texture 

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References

  1. 1.
    Agrawal, A., Raskar, R., Nayar, S.K., Li, Y.: Removing photography artifacts using gradient projection and flash-exposure sampling. ACM Tr. on Graphics 24(3), 828–835 (2005)CrossRefGoogle Scholar
  2. 2.
    Ashdown, I.: Near-Field Photometry: Measuring and Modeling Complex 3-D Light Sources. In: ACM SIGGRAPH 1995 Course Notes, pp. 1–15 (1995)Google Scholar
  3. 3.
    Barnard, K., Cardei, V., Funt, B.: A comparison of computational color constancy algorithms. I: Methodology and experiments with synthesized data. Image Processing, IEEE Trans. 11(9), 972–984 (2002)CrossRefGoogle Scholar
  4. 4.
    Barnard, K., Martin, L., Coath, A., Funt, B.: A Comparison of Computational Color Constancy Algorithms Part II: Experiments With Image Data. IEEE Trans. on Image processing 11(9), 985 (2002)CrossRefGoogle Scholar
  5. 5.
    Callieri, M., Cignoni, P., Corsini, M., Scopigno, R.: Masked photo blending: mapping dense photographic dataset on high-resolution 3d models. Computer & Graphics 32(4), 464–473 (2008)CrossRefGoogle Scholar
  6. 6.
    Commission Internationale de l’Eclairage (CIE). Colorimetry CIE 15 (2004)Google Scholar
  7. 7.
    Debevec, P.: Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography. In: SIGGRAPH 1998, pp. 189–198. ACM Press, New York (1998)Google Scholar
  8. 8.
    Ebner, M.: Color constancy using local color shifts. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 276–287. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Eisemann, E., Durand, F.: Flash photography enhancement via intrinsic relighting. In: ACM Trans. on Graphics, vol. 23, ACM Press, New York (2004)Google Scholar
  10. 10.
    Finlayson, G.D., Hordley, S.D.: Improving gamut mapping color constancy. IEEE Trans. on Image Processing 9(10), 1774–1783 (2000)CrossRefGoogle Scholar
  11. 11.
    Finlayson, G.D., Hordley, S.D., Hubel, P.M.: Color by correlation: A simple, unifying framework for color constancy. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(11), 1209–1221 (2001)CrossRefGoogle Scholar
  12. 12.
    Gijsenij, A., Gevers, T.: Color constancy using natural image statistics. In: Int. Conf. on Comp. Vision and Pat. Recogn., Minneapolis, USA, June 2007, pp. 1–8 (2007)Google Scholar
  13. 13.
    Goesele, M., Granier, X., Heidrich, W., Seidel, H.-P.: Accurate light source acquisition and rendering. ACM Tr. Gr. 22(3), 621–630 (2003)CrossRefGoogle Scholar
  14. 14.
    Gortler, S.J., Grzeszczuk, R., Szeliski, R., Cohen, M.F.: The lumigraph. In: SIGGRAPH 1996, pp. 43–54 (1996)Google Scholar
  15. 15.
    Grubbs, F.: Procedures for detecting outlying observations in samples. Technometrics 11, 1–21 (1969)CrossRefGoogle Scholar
  16. 16.
    Heidrich, W., Kautz, J., Slusallek, P., Seidel, H.-P.: Canned lightsources. In: Rend. Tech., pp. 293–300 (1998)Google Scholar
  17. 17.
    Hoppe, H., Toyama, K.: Continuous flash. Technical Report MSR-TR-2003-63, Microsoft Research (2003)Google Scholar
  18. 18.
    Hsu, E., Mertens, T., Paris, S., Avidan, S., Durand, F.: Light mixture estimation for spatially varying white balance. In: SIGGRAPH 2008, ACM Press, New York (2008)Google Scholar
  19. 19.
    Kawakami, R., Ikeuchi, K., Tan, R.T.: Consistent surface color for texturing large objects in outdoor scenes. In: ICCV 2005: Int. Conf. on Computer Vision, Washington, DC, USA, pp. 1200–1207 (2005)Google Scholar
  20. 20.
    Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Tr. Gr. 25(3), 646–653 (2006)CrossRefGoogle Scholar
  21. 21.
    Lu, C., Drew, M.S., Finlayson, G.D.: Shadow removal via flash/noflash illumination. In: W. on Mult. Signal Processing, pp. 198–201 (2006)Google Scholar
  22. 22.
    Omer, I., Werman, M.: Color lines: Image specific color representation. In: CVPR 2004, June 2004, vol. II, pp. 946–953. IEEE, Los Alamitos (2004)Google Scholar
  23. 23.
    Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., Toyama, K.: Digital photography with flash and no-flash image pairs. In: SIGGRAPH 2004, pp. 664–672 (2004)Google Scholar
  24. 24.
    Unger, J., Wenger, A., Hawkins, T., Gardner, A., Debevec, P.: Capturing and rendering with incident light fields. In: EGRW 2003, pp. 141–149 (2003)Google Scholar
  25. 25.
    van de Weijer, J., Gevers, T.: Color constancy based on the grey-edge hypothesis. In: ICIP, pp. 722–725 (2005)Google Scholar
  26. 26.
    Vergauwen, M., Van Gool, L.: Web-based 3d reconstruction service. Mach. Vision Appl. 17(6), 411–426 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Matteo Dellepiane
    • 1
  • Marco Callieri
    • 1
  • Massimiliano Corsini
    • 1
  • Paolo Cignoni
    • 1
  • Roberto Scopigno
    • 1
  1. 1.Visual Computing LabISTI-CNRPisaItaly

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