lecture10

Histogram Equalization
Is a popular technique for improving the appearance of a poor image.
It s a function is similar to that of a histogram stretch but often provides
more visually pleasing results a cross a wide rang of images.
Histogram equalization is a technique where the histogram of the
resultant image is as flat as possible (with histogram stretching the overall
shape of the histogram remains the same).
The results in a histogram with a mountain grouped closely together to
"spreading or flatting histogram makes the dark pixels appear darker and
the light pixels appear lighter (the key word is "appear" the dark pixels in
a photograph can not by any darker. If, however, the pixels that are only
slightly lighter become much lighter, then the dark pixels will appear
darker).
The histogram equalization process for digital images consists of four
steps:
1. Find the running sum of the histogram values
2. Normalize the values from step1 by dividing by total number of
pixels.
3. Multiply the values from step2 by the maximum gray level value and
round.
4. Map the gray-level values to the results from step 3, using a one-toone
correspondence. The following example will help to clarify this
process.
2
Example:-
We have an image with 3 bit /pixel, so the possible range of values is 0 to
7. We have an image with the following histogram: