Contrast Enhancement

Nonlinear Contrast Enhancement
 


Nonlinear contrast enhancement often involves histogram equalizations through the use of an algorithm. The nonlinear contrast stretch method has one major disadvantage. Each value in the input image can have several values in the output image, so that objects in the original scene lose their correct relative brightness values.

Histogram Equalization

Histogram equalization is one of the most useful forms of nonlinear contrast enhancement. When an image's histogram is equalized, all pixel values of the image are redistributed so there are approximately an equal number of pixels to each of the user-specified output gray-scale classes (e.g., 32, 64, and 256). Contrast is increased at the most populated range of brightness values of the histogram (or "peaks"). It automatically reduces the contrast in very light or dark parts of the image associated with the tails of a normally distributed histogram (Jensen 1996). Histogram equalization can also seperate pixels into distinct groups, if there are few output values over a wide range.

Figure 6-3.7 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 results in the loss of the dark and bright characteristics usually associated with the tail pixels (ERDAS Inc, 1995).

Figure 6-3.7
Histogram
Before Equalization		 Histogram
After 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 pixels that are very dark or very bright into very few gray 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 of Charelston, South Carolina is shown in Figure 6-3.8. Notice the change in each of the histograms as values in the tails are grouped together.

Figure 6-3.8
Normal Histogram
TM Band 4		 Equalized Histogram
TM Band 4
 


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