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Color Compensation Using Nonlinear Luminance-RGB Component Curve of a Camera

  • Sejung Yang
  • Yoon-Ah Kim
  • Chaerin Kang
  • Byung-Uk Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6939)

Abstract

Many color image processing methods employ gray scale image algorithms first and then apply color mapping afterwards. Most popular gamut mapping techniques are hue-preserving methods such as shifting or scaling of color components. However, those methods result in unnatural modification of color saturation. In this paper, we propose a novel color mapping method based on nonlinear luminance-RGB component characteristic curves of a camera taking the image. We obtain an accurate color value after luminance enhancement using luminance-RGB curves of the camera. All experiments demonstrate that our algorithm is effective and suitable to be embedded in a camera because of its simplicity and accuracy in color enhancement of digital images.

Keywords

Subjective Image Quality Color Saturation Luminance Change Ground Truth Image High Dynamic Range Imaging 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Pratt, W.K.: Digital Image Processing, 3rd edn. Wiley Interscience, Hoboken (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Yang, C.C., Rodriguez, J.J.: Efficient luminance and saturation processing techniques for bypassing color coordinate transformations. In: Proc. IEEE Int. Conf. on Systems, Man, and Cybernetics, vol. 1, pp. 667–672 (1995)Google Scholar
  3. 3.
    Trahanias, P.E., Venetsanopoulos, A.N.: Color image enhancement through 3-D histogram equalization. In: Proc. 15th IAPR Int. Conf. Pattern Recognition, vol. 1, pp. 545–548 (August-September 1992)Google Scholar
  4. 4.
    Menotti, D., Najman, L., de Albuquerque, A., Facon, J.: A Fast Hue-Preserving Histogram Equalization Method for Color Image Enhancement using a Bayesian Framework. In: Proc. 14th International Workshop on Systems, Signal & Image Processing (IWSSIP), pp. 414–417 (June 2007)Google Scholar
  5. 5.
    Naik, S., Murthy, C.: Hue-preserving color image enhancement without gamut problem. IEEE Trans. Image Processing 12(12), 1591–1598 (2003)CrossRefGoogle Scholar
  6. 6.
    Huang, Y., Hui, L., Goh, K.H.: Hue-based color saturation compensation. In: IEEE International Conference on Consumer Electronics, pp. 160–164 (September 2004)Google Scholar
  7. 7.
    Lee, H.-W., Yang, S., Lee, B.-U.: Color compensation of histogram equalized images. In: IS&T/SPIE Electronic Imaging, SPIE, vol. 7241, pp. 724111-1–9 (January 2009)Google Scholar
  8. 8.
    Arici, T., Dikbas, S., Altunbasak, Y.: A Histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Processing 18(9), 1921–1935 (2009)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Debevec, P.E., Malik, J.: Recovering High Dynamic Range Radiance Maps from Photographs. In: Proc. the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 369–378 (1997)Google Scholar
  10. 10.
    Hasinoff, S.W., Durand, F., Freeman, W.: Noise-Optimal Capture for High Dynamic Range Photography. In: Proc. Computer Vision and Pattern Recognition, pp. 553–560 (2010)Google Scholar
  11. 11.
    Matusik, W., Pfister, H., Ngan, A., Beardsley, P., Ziegler, R., McMillan, L.: Image-Based 3D Photography Using Opacity Hulls. In: SIGGRAPH 2002, pp. 427–437 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sejung Yang
    • 1
  • Yoon-Ah Kim
    • 1
  • Chaerin Kang
    • 1
  • Byung-Uk Lee
    • 1
  1. 1.Dept. of Electronics EngineeringEwha Womans UniversitySeoulKorea

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