Fast Automatic Compensation of Under/Over- Exposured Image Regions

  • Vassilios Vonikakis
  • Ioannis Andreadis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


This paper presents a new algorithm for spatially modulated tone mapping in Standard Dynamic Range (SDR) images. The method performs image enhancement by lightening the tones in the under-exposured regions while darkening the tones in the over-exposured, without affecting the correctly exposured ones. The tone mapping function is inspired by the shunting characteristics of the center-surround cells of the Human Visual System (HVS). This function is modulated differently for every pixel, according to its surround. The surround is calculated using a new approach, based on the oriented cells of the HVS, which allows it to adapt its shape to the local contents of the image and, thus, minimize the halo effects. The method has low complexity and can render 1MPixel images in approximately 1 second when executed by a conventional PC.


Image Enhancement Tone Mapping Human Visual System 


  1. 1.
    Battiato, S., Castorina, A., Mancuso, M.: High dynamic range imaging for digital still camera: an overview. Journal of Electronic Imaging 12, 459–469 (2003)CrossRefGoogle Scholar
  2. 2.
    Land, E.: The Retinex. American Scientist 52(2), 247–264 (1964)Google Scholar
  3. 3.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: A multi-scale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Processing 6, 965–976 (1997)CrossRefGoogle Scholar
  4. 4.
    Rizzi, A., Gatta, C., Marini, D.: A new algorithm for unsupervised global and local color correction. Pattern Recognition Letters 24, 1663–1677 (2003)CrossRefGoogle Scholar
  5. 5.
    Ellias, S., Grossberg, S.: Pattern formation, contrast control and oscillations in the short term memory of shunting on-center off-surround networks. Biological Cybernetics 20, 69–98 (1975)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Truview (2007),
  7. 7.
  8. 8.
  9. 9.
    Hasler, S., Susstrunk, S.: Measuring colorfulness in real images. In: Proc. SPIE Electron. Imag.: Hum. Vision Electron. Imag. VIII, SPIE 5007, pp. 87–95 (2003)Google Scholar
  10. 10.
    Huang, K.-Q., Wang, Q., Wu, Z.-Y.: Natural color image enhancement and evaluation algorithm based on human visual system. Computer Vision and Image Understanding 103, 52–63 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Vassilios Vonikakis
    • 1
  • Ioannis Andreadis
    • 1
  1. 1.Democritus University of Thrace, Department of Electrical and Computer Engineering, Laboratory of Electronics, Section of Electronics and Information Systems Technology, Vas. Sofias, GR-67100 XanthiGreece

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