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Digital Image Processing
Gray Level Enhancement
Dr. Muhammad Jehanzeb
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Gray level enhancement
Some of the simplest, yet most useful,
image processing operations involve theadjustment of brightness, contrast or color in
an image. This operation is called contrast
enhancement.
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Gray level enhancement
• common reason for manipulating theseattributes is the need to compensate fordifficulties in image ac!uisition.
• "ontrast enhancement aims to improve animage such that the content of an image
become visually more pleasing.
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dar# image
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n enhanced image
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There are several techni!ues in enhancingthe contrast of digital image such ashistogram stretch, histogram e!uali$ationand adaptive contrast enhancement.
%istogram e!uali$ation is a popular
techni!ue for improving the appearance of a poor image.
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Its function is similar to that of a histogram
stretch but often provides more visually pleasing results across &ider range of
images.
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%istogram '!uali$ation lgorithm
%istogram '!uali$ation is a techni!ue &here the
histogram of the resultant image is as flat as
possible.
(ith histogram stretching the overall shape of the
histogram remains the same.
The theoretical basis for histogram e!uali$ation
involves probability theory, &here the histogram is
treated as the probability distribution of the gray
levels.
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%istogram '!uali$ation lgorithm
The histogram e!uali$ation process for digital image
consists of four steps)
i. *ind the running sum of the histogram valuesii. +ormali$e the values from step i- by dividing by the
total number of piels
iii. /ultiply the values from step ii- by maimum gray
level value and round
iv. /ap the gray level values to the results from step iii-‑
using a one to one correspondence‑ ‑
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Example: Given a 0 bits1image, so the possible range
of values is 2 to 3. Suppose the image has the
follo&ing histogram)
%istogram of image
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%istogram of original image
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steps to find the histogram1e!uali$ed values.
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In order to get the histogram e!uali$ed‑
image, all piels in the original image &ith
gray level 2 are set to 4, values of 4 are set 5,5 set 6, 6 set to 6, and so on
Table sho&s the histogram of the
histogram e!uali$ed image.‑
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%istogram of the histogram1e!uali$ed image
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%istogram of histogram1e!uali$ed image
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