Image Histogram Equalization: Normal and Equalized Histogram (Especially for GATE-Geospatial 2022)

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The underlying principle of histogram equalization is straightforward and simple, it is assumed that each level in the displayed image should contain an approximately equal number of pixel values so that the histogram of these displayed values is almost uniform (though not all 256 classes are necessarily occupied) . The objective of the histogram equalization is to spread the range of pixel values present in the input image over the full range of the display device.

Process of Histogram Equalization

  • Below figure shows two histograms. The first histogram shows values before equalization is performed. When this histogram is compared to the equalized histogram, one can see that the enhanced image gains contrast in the most populated areas of the original histogram. In this example, the input range of 3 to 7 is stretched to the range of 1 to 8.
  • However, the data values at the tails of the original histogram are grouped together. Input values 0 through 2 all have the output values of 0. This result in the loss of the dark and bright characteristics usually associated with the tail pixels (ERDAS Inc, 1995) .
New and Old Histogram

Loss During Histogram Equalization

  • Image analysts must be aware that while histogram equalization often provides an image with the most contrast of any enhancement technique, it may hide much-needed information.
  • This technique groups pixel that are very dark or very bright into very few grey scales. If one is trying to bring out information about data in terrain shadows, or there are clouds in your data, histogram equalization may not be appropriate.
  • An original and equalized image is shown in below figure. Important is the change in each of the histograms as values in the tails are grouped together.
Example of Histogram Equalization

Example of Histogram equalization

Gaussian Stretch

This method of contrast enhancement is based upon the histogram of the pixel values is called a Gaussian stretch because it involves the fitting of the observed histogram to a normal or Gaussian histogram. We will study more on this in contrast stretching.

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