| Image
Processing Articles
Index |
 |
| What
is Histogram |
| The
histogram in the context
of image processing
is the operation by
which the occurrences
of each intensity
value in the image
is shown. Normally,
the histogram is a
graph showing the
number of pixels in
an image at each different
intensity value found
in that image. For
an 8-bit grayscale
image there are 256
different possible
intensities, and so
the histogram will
graphically display
256 numbers showing
the distribution of
pixels amongst those
grayscale values.
More about the histogram
can be found in the
Histogram/Normalized
Histogram Operation
article. |
|
| What
is Histogram Equalization |
| Histogram
equalization is the
technique by which
the dynamic range
of the histogram of
an image is increased.
Histogram equalization
assigns the intensity
values of pixels in
the input image such
that the output image
contains a uniform
distribution of intensities.
It improves contrast
and the goal of histogram
equalization is to
obtain a uniform histogram.
This technique can
be used on a whole
image or just on a
part of an image. |
|
| Histogram
equalization redistributes
intensity distributions.
If the histogram of
any image has many
peaks and valleys,
it will still have
peaks and valley after
equalization, but
peaks and valley will
be shifted. Because
of this, "spreading"
is a better term than
"flattening"
to describe histogram
equalization. In histogram
equalization, each
pixel is assigned
a new intensity value
based on the its previous
intensity level. |
|
| General
Working |
|
| The
histogram equalization
is operated on an
image in three step: |
|
1).
Histogram Formation
2). New Intensity
Values calculation
for each Intensity
Levels
3). Replace the previous
Intensity values with
the new intensity
values
For the first step
see the article on
histogram.
In step 2, new intensity
values are calculated
for each intensity
level by applying
the following equation: |
|
 |
|
| The
meaning of Max. Intensity
Levels maximum intensity
level which a pixel
can get. For example,
if the image is in
the grayscale domain,
then the count is
255. And if the image
is of size 256x256
then, the No. of pixels
is 65536. And the
expression is the
bracket means that
the no. of pixels
having the intensity
below the output intensity
level or equal to
it. For example, if
we are calculating
the output intensity
level for 1 input
intensity level, then
the it means that
the no. of pixels
in the image having
the intensity below
or equal to 1 means
0 and 1. If we are
calculating the output
intensity level for
5 input intensity
level, then the it
means that the no.
of pixels in the image
having the intensity
below or equal to
5 means 0 , 1 , 2
, 3 , 4 , 5. Thus,
if we are calculating
the output intensity
level for 255 input
intensity level, then
the it means that
the no. of pixels
in the image having
the intensity below
or equal to 255 means
0 , 1 , 2 , 3 , ......
, 255. That is how
new intensity levels
are calculated for
the previous intensity
levels. |
|
| The
next step is to replace
the previous intensity
level with the new
intensity level. This
is accomplished by
putting the value
of Oi in the image
for all the pixels,
where Oi represents
the new intensity
value, whereas i represents
the previous intensity
level. |
|
| Guidelines
for Use |
| To
understand the working
of the histogram equalization,
take the example of
the following image: |
|
 |
|
| the
dynamic range of image
intensities is shown
by the following histogram: |
|
 |
|
| when
the histogram equalization
operation is performed
on this image, the
effects can be shown
by the following equalized
histogram: |
|
 |
|
| and
the following image
shows the achieved
image: |
|
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|
|
| Sample
Project |
|
| The
interface for the
sample project for
the histogram equalization
is the same as for
the histogram. So,
the readers are encouraged
to read the Histogram/Normalized
Histogram Operation
article, before they
start to read this
article. |
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Project Files
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|
| Image
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