Greedy Algorithm for Local Contrast Enhancement of Images

  • Kartic Subr
  • Aditi Majumder
  • Sandy Irani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


We present a technique that achieves local contrast enhancement by representing it as an optimization problem. For this, we first introduce a scalar objective function that estimates the average local contrast of the image; to achieve the contrast enhancement, we seek to maximize this objective function subject to strict constraints on the local gradients and the color range of the image. The former constraint controls the amount of contrast enhancement achieved while the latter prevents over or under saturation of the colors as a result of the enhancement. We propose a greedy iterative algorithm, controlled by a single parameter, to solve this optimization problem. Thus, our contrast enhancement is achieved without explicitly segmenting the image either in the spatial (multi-scale) or frequency (multi-resolution) domain. We demonstrate our method on both gray and color images and compare it with other existing global and local contrast enhancement techniques.


Greedy Algorithm Color Gamut Sweep Plane Retinex Theory Color Image Enhancement 
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.


  1. 1.
    Oakley, J.P., Satherley, B.L.: Improving image quality in poor visibility conditions using a physical model for contrast degradation. IEEE Transactions on Image Processing 7, 167–179 (1998)CrossRefGoogle Scholar
  2. 2.
    Boccignone, G., Picariello, A.: Multiscale contrast enhancement of medical images. In: Proceedings of ICASSP (1997)Google Scholar
  3. 3.
    Toet, A.: Multi-scale color image enhancement. Pattern Recognition Letters 13, 167–174 (1992)CrossRefGoogle Scholar
  4. 4.
    Toet, A.: A hierarchical morphological image decomposition. Pattern Recognition Letters 11, 267–274 (1990)zbMATHCrossRefGoogle Scholar
  5. 5.
    Mukhopadhyay, S., Chanda, B.: Hue preserving color image enhancement using multi-scale morphology. In: Indian Conference on Computer Vision, Graphics and Image Processing (2002)Google Scholar
  6. 6.
    Burt, P.J., Adelson, E.H.: A multiresolution spline with application to image mosaics. ACM Transactions on Graphics 2, 217–236 (1983)CrossRefGoogle Scholar
  7. 7.
    Hanmandlu, M., Jha, D., Sharma, R.: Localized contrast enhancement of color images using clustering. In: Proceedings of IEEE International Conference on Information Technology: Coding and Computing (ITCC) (2001)Google Scholar
  8. 8.
    Munteanu, C., Rosa, A.: Color image enhancement using evolutionary principles and the retinex theory of color constancy. In: Proceedings 2001 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing XI, pp. 393–402 (2001)Google Scholar
  9. 9.
    Rahman, Z., Jobson, D.J., Woodell, G.A.: Multi-scale retinex for color image enhancement. In: IEEE International Conference on Image Processing (1996)Google Scholar
  10. 10.
    Velde, K.V.: Multi-scale color image enhancement. In: Proceedings on International Conference on Image Processing, vol. 3, pp. 584–587 (1999)Google Scholar
  11. 11.
    Stark, J.L., Murtagh, F., Candes, E.J., Donoho, D.L.: Gray and color image contrast enhancement by curvelet transform. IEEE Transactions on Image Processing 12 (2003)Google Scholar
  12. 12.
    Hanmandlu, M., Jha, D., Sharma, R.: Color image enhancement by fuzzy intensification. In: Proceedings of International Conference on Pattern Recognition (2000)Google Scholar
  13. 13.
    Shyu, M., Leou, J.: A geneticle algorithm approach to color image enhancement. Pattern Recognition 31, 871–880 (1998)CrossRefGoogle Scholar
  14. 14.
    Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Transactions on Graphics, Proceedings of ACM Siggraph 21, 249–256 (2002)Google Scholar
  15. 15.
    Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Transactions on Graphics, Proceedings of ACM Siggraph 22, 313–318 (2003)Google Scholar
  16. 16.
    Valois, R.L.D., Valois, K.K.D.: Spatial Vision. Oxford University Press, Oxford (1990)Google Scholar
  17. 17.
    Giorgianni, E.J., Madden, T.E.: Digital Color Management: Encoding Solutions. Addison-Wesley, Reading (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Kartic Subr
    • 1
  • Aditi Majumder
    • 1
  • Sandy Irani
    • 1
  1. 1.School of Information and Computer ScienceUniversity of CaliforniaIrvine

Personalised recommendations