“Light mixture estimation for spatially varying white balance” by Hsu, Mertens, Paris, Avidan and Durand

  • ©Eugene Hsu, Tom Mertens, Sylvain Paris, Shai Avidan, and Frédo Durand

Conference:


Type:


Title:

    Light mixture estimation for spatially varying white balance

Presenter(s)/Author(s):



Abstract:


    White balance is a crucial step in the photographic pipeline. It ensures the proper rendition of images by eliminating color casts due to differing illuminants. Digital cameras and editing programs provide white balance tools that assume a single type of light per image, such as daylight. However, many photos are taken under mixed lighting. We propose a white balance technique for scenes with two light types that are specified by the user. This covers many typical situations involving indoor/outdoor or flash/ambient light mixtures. Since we work from a single image, the problem is highly underconstrained. Our method recovers a set of dominant material colors which allows us to estimate the local intensity mixture of the two light types. Using this mixture, we can neutralize the light colors and render visually pleasing images. Our method can also be used to achieve post-exposure relighting effects.

References:


    1. Barnard, K., Finlayson, G. D., and Funt, B. V. 1997. Color constancy for scenes with varying illumination. Computer Vision and Image Understanding 65, 2 (Mar.), 311–321. Google ScholarDigital Library
    2. Brainard, D. H., and Freeman, W. T. 1997. Bayesian color constancy. Journal of the Optical Society of America 14, 7 (July), 1393–1411.Google Scholar
    3. Buchsbaum, G. 1980. A spatial processor model for object colour perception. Journal of The Franklin Institute 310, 1 (July), 1–26.Google ScholarCross Ref
    4. Chong, H., Gortler, S., and Zickler, T. 2007. The von Kries hypothesis and a basis for color constancy. In IEEE International Conference on Computer Vision, 1–8.Google Scholar
    5. Ebner, M. 2004. Color constancy using local color shifts. In European Conference on Computer Vision, 276–287.Google ScholarCross Ref
    6. Finlayson, G. D., and Hordley, S. D. 2000. Improving gamut mapping color constancy. IEEE Transactions on Image Processing 9, 10 (Oct.), 1774–1783. Google ScholarDigital Library
    7. Finlayson, G. D., Hordley, S. D., and Hubel, P. M. 2001. Color by correlation: A simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 11 (Nov.), 1209–1221. Google ScholarDigital Library
    8. Finlayson, G. D. 1995. Color constancy in diagonal chromaticity space. In IEEE International Conference on Computer Vision, 218–223. Google ScholarDigital Library
    9. Forsyth, D. A. 1990. A novel algorithm for color constancy. International Journal of Computer Vision 5, 1 (Aug.), 5–36. Google ScholarDigital Library
    10. Gijsenij, A., and Gevers, T. 2007. Color constancy using natural image statistics. In IEEE Computer Vision and Pattern Recognition, 1–8.Google Scholar
    11. Hough, P. V. C., 1962. Method and means of recognizing complex patterns. U.S. Patent 3,069,654.Google Scholar
    12. Kawakami, R., Ikeuchi, K., and Tan, R. T. 2005. Consistent surface color for texturing large objects in outdoor scenes. In IEEE International Conference on Computer Vision, 1200–1207. Google ScholarDigital Library
    13. Kopf, J., Cohen, M. F., Lischinski, D., and Uyttendaele, M. 2007. Joint bilateral upsampling. ACM Transactions on Graphics 26, 3 (July), 96:1–96:5. Google ScholarDigital Library
    14. Land, E. H., and McCann, J. J. 1971. Lightness and Retinex theory. Journal of the Optical Society of America 61, 1 (Jan.), 1–11.Google ScholarCross Ref
    15. Levin, A., Lischinski, D., and Weiss, Y. 2004. Colorization using optimization. ACM Transactions on Graphics 23, 3 (Aug.), 689–694. Google ScholarDigital Library
    16. Levin, A., Lischinski, D., and Weiss, Y. 2006. A closed form solution to natural image matting. In IEEE Computer Vision and Pattern Recognition, 61–68. Google ScholarDigital Library
    17. Lischinski, D., Farbman, Z., Uyttendaele, M., and Szeliski, R. 2006. Interactive local adjustment of tonal values. ACM Transactions on Graphics 25, 3 (July), 646–653. Google ScholarDigital Library
    18. Omer, I., and Werman, M. 2004. Color lines: Image specific color representation. In Computer Vision and Pattern Recognition, 946–953. Google ScholarDigital Library
    19. Van de Weijer, J., and Gevers, T. 2005. Color constancy based on the grey-edge hypothesis. In IEEE International Conference on Image Processing, 722–725.Google Scholar
    20. Wandell, B. A. 1995. Foundations of Vision. Sinauer Associates, Sunderland, MA.Google Scholar


ACM Digital Library Publication:



Overview Page: