Color Image Histogram Equalization

Histogram equalization is a simple and effective method for image contrast manipulation. It aims together with other methods, such as noise reduction, edge crispening and sharpening, filtering, pseudocoloring, image interpolation and magnification to enhance images either from the human visual perspective or for their effective use in several applications.

Histogram equalization becomes a tedious task when dealing with color images due to:

  • the vectorial nature of color: each color pixel is represented by a vector with as many components as the color components in a proper color space (i.e., the three components Red, Green, and Blue in the RGB space)
  • the correlation between the color components
  • the color perception by humans.

 

Our Method

A novel color image histogram equalization method has been developed that

  • works on the HSI color space leaving Hue unmodified
  • exploits the notion of unigram and bigram probabilities borrowed from statistical language modeling
  • conducts probability smoothing by an absolute discounting back-off technique (before the equalization process)

in order to respectively

  • preserve color information
  • jointly equalize the Saturation and intensity Components
  • deal with the unseen color component combinations stemming from the dimensionality of the color space and the limited number of colors present in an image.

The method has been furtherly extended by an empirical gamut elimination technique based on the transformations proposed by Naik and Murhy in order to deal with colors in the equalized image that lie outside the color space gamut.

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Relevant Publications

N.Bassiou and C. Kotropoulos, “Color Image Histogram Equalization by Absolute Discounting Back-off ”, in revision for publication in Elsevier Computer Vision and Image Understanding.

N.Bassiou and C. Kotropoulos, “Color Histogram Equalization using Probability Smoothing” , in Proc. of European Signal Processing Conference (EUSIPCO 2006), Florence, Italy, 4-8 September, 2006.

 

Research Projects

MUSCLE “Multimedia Understanding through Semantics, Computation and LEarning” (FP6-507752)

8/11/2006 Bassiou Nikoletta