In the era of
image processing, scientific analysis, digital photography, remote sensing and
in visualization, medical image analysis, surveillance system; image
enhancement plays a vital role. By enhancement of image noise can be reduced
and it can remove artifacts. A special feature of image enhancement is that it
can hold all the details of image after enhancement.
As we know
contrast enhancement of an image is done by making light colors lighter and
dark colors darker at the same time. And this process is done by setting all
color components below a specified lower bound to zero, and all color
components above a specified upper bound to the maximum intensity. Here various
methods of histogram equalization is addressed. Histogram graphically shows the
distribution of pixels among grey scale values. Dynamic range of an image can
be improved by equalization method. Histogram equalization is an efficient and
useful technique.
The intensities
will be equally distributed in output image after the process of histogram
equalization.
There are some reasons
that led to the need of enhancement:
- bad quality of the used imaging device,
- lack of expertise of the operator
- The adverse external conditions or environment condition at the time of capture.
IMAGE
ENHANCEMENT
Processing of
images to extract some specific features of an image is called image
enhancement. The main motive of image enhancement is to improve the original image
for some specific applications.
It
sharpens or improves image features such as boundaries, or contrast to make a
better graphic display, and better analysis
Image
enhancement has two categories:
1) Spatial
domain method
2) Frequency
domain method
Spatial domain
method is based on direct manipulation of pixels in an image. Frequency domain
method is based on modifying FT of an image.
Enhancement of
an image is done by sharpening, noise removal and brightness increment.
Unfortunately there is no general theory for determining what ‘good’ image
enhancement, when it comes to human perception. If it looks good .it is good!
Operation of
image enhancement is shown by given block diagram
 |
| Operation of Image Enhancement |
Histogram equalization is
distribution of particular type of data. It plays a vital role in image processing. By histogram equalization we can
improve contrast and appearance of an image. Entire spectrum of pixels (0-255)
will be stretches by histogram equalization. A histogram that covers all possible
values which is used by gray scale is determined as a good histogram. A good
histogram tends to have good contrast and the details of an image that may be
easily observed.
In particular,
the method can lead to better views of bone structure in x-ray images, and to
better detail in photographs that are over or under-exposed.
Here some
advantage and disadvantage of this method
Advantage: A key advantage
of the method is that it is a fairly straight forward technique and an
invertible operator. So in theory, if the histogram equalization function is
known, then the original histogram can be recovered.
Disadvantage: A disadvantage
of the method is that it is indiscriminate. It may increase the contrast of
background noise, while decreasing the usable signal.
Histogram
equalization is a specific case of the more general class of histogram
remapping Methods. These methods seek to adjust the image to make it easier to
analyze or improve the visual quality.
As the methods
of histogram equalization are histogram expansion, cumulative distributive
equalization, par sectioning, odd sectioning, and local area histogram
equalization.
Process of
histogram equalization using cumulative distribution function have described in
given figure.
Process of histogram equalization
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