Automatic Contrast Enhancement Using Pixel-Based Calibrating and Mean Shift Clustering

  • Yu-Yi Liao
  • Jzau-Sheng Lin
  • Ping-Jui Liu
  • Shen-Chuan Tai
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 128)

Abstract

In this paper, we present the method for automatic contrast enhancement of color image. The base concept of method is that an image has its own reference luminance level and each pixel has its own characteristic luminance that is brighter or darker than reference luminance level. In the proposed method, a given color image is converted to HSV color space from RGB color space firstly. Next, each pixel in the image find out the own characteristic luminance based on the reference luminance level. The characteristic luminance is calibrated to the target luminance that will get the acceptable luminance. We apply alpha blending the original luminance and characteristic luminance to reduce the HALO artifact and preserve details of darker area by mean shift clustering.

Keywords

Color Image Dark Area Luminance Component Target Luminance Adaptive Histogram Equalization 
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.
    Beghdadi, A., Negrate, A.L.: Contrast enhancement technique based on local detection of edges. Computer Vision, Graphics, and Image Processing 46(2), 162–174 (1989)CrossRefGoogle Scholar
  2. 2.
    Tsai, C.-M., Yeh, Z.-N.: Contrast Enhancement by Automatic and Parameter-Free Piecewise Linear Transformation for Color Images. IEEE Transactions on Consumer Electronics 54(2), 213–219 (2008)CrossRefGoogle Scholar
  3. 3.
    Jobson, D.J., Rahman, Z.-U., Woodell, G.A.: Properties and Performance of a Center/Surround Retinex. IEEE Transactions on Image Processing 6(3), 451–462 (1997)CrossRefGoogle Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Machine Intell. 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Han, H., Sohn, K.: Automatic Illumination and Color Compensation Using Mean Shift and Sigma Filter. IEEE Transactions on Consumer Electronics 55(3), 978–986 (2009)CrossRefGoogle Scholar
  6. 6.
    Starck, J.-L., Murtagh, F., Candes, E.J., Donoho, D.L.: Gray and Color Image Contrast Enhancement by the Curvelet Transform. IEEE Transactions on Image Processing 12(6), 706–717 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Panetta, K.A., Wharton, E.J., Agaian, S.S.: Human Visual System-Based Image Enhancement and Logarithmic Contrast Measure. IEEE Transaction on Systems, Man and Cybernetics Part B: Cybernetics 38(1), 174–188 (2008)CrossRefGoogle Scholar
  8. 8.
    Jin, Y., Fayad, L., Laine, A.: Contrast enhancement by multi-scale adaptive histogram equalization. In: Proc. SPIE, vol. 4478, pp. 206–213 (2001)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Yu-Yi Liao
    • 1
  • Jzau-Sheng Lin
    • 2
  • Ping-Jui Liu
    • 2
  • Shen-Chuan Tai
    • 1
  1. 1.Institute Computer and Communication EngineeringNational Cheng Kung UniversityTainanTaiwan
  2. 2.Department of Computer Science and Information EngineeringNational Chin-Yi University of TechnologyTaichungTaiwan

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