| Image
Processing Articles
Index |
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| Brief
Description |
| The
median filter is normally
used to reduce noise
in an image, somewhat
like the mean filter.
However, it often
does a better job
than the mean filter
of preserving useful
detail in the image.
Median filtering is
a non-linear signal
enhancement technique
for the smoothing
of signals, the suppression
of impulse noise,
and preserving of
edges. In the one-dimensional
case it consists of
sliding a window of
an odd number of elements
along the signal,
replacing the centre
sample by the median
of the samples in
the window. |
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| How
It Works |
|
| Like
the mean filter, the
median filter considers
each pixel in the
image in turn and
looks at its nearby
neighbors to decide
whether or not it
is representative
of its surroundings.
Instead of simply
replacing the pixel
value with the mean
of neighboring pixel
values, it replaces
it with the median
of those values. The
median is calculated
by first sorting all
the pixel values from
the surrounding neighborhood
into numerical order
and then replacing
the pixel being considered
with the middle pixel
value. (If the neighborhood
under consideration
contains an even number
of pixels, the average
of the two middle
pixel values is used.)
Figure 1 illustrates
an example calculation. |
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 |
|
| Calculating
the median value of
a pixel neighborhood.
As can be seen, the
central pixel value
of 150 is rather unrepresentative
of the surrounding
pixels and is replaced
with the median value:
124. A 3×3 square
neighborhood is used
here --- larger neighborhoods
will produce more
severe smoothing. |
|
| In
general, the median
filter allows a great
deal of high spatial
frequency detail to
pass while remaining
very effective at
removing noise on
images where less
than half of the pixels
in a smoothing neighborhood
have been effected.
(As a consequence
of this, median filtering
can be less effective
at removing noise
from images corrupted
with Gaussian noise.) |
|
| One
of the major problems
with the median filter
is that it is relatively
expensive and complex
to compute. To find
the median it is necessary
to sort all the values
in the neighborhood
into numerical order
and this is relatively
slow, even with fast
sorting algorithms
such as quicksort.
The basic algorithm
can, however,be enhanced
somewhat for speed.
A common technique
is to notice that
when the neighborhood
window is slid across
the image, many of
the pixels in the
window are the same
from one step to the
next, and the relative
ordering of these
with each other will
obviously not have
changed. Clever algorithms
make use of this to
improve performance. |
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| Basic
Working: |
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| The
effect of a median
Filter on the Image: |
|
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| The
result after convolution
is as: |
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| Sample
Project |
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| The
GUI of the whole Project
is as: |
|
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| The
project is a part
of the series of the
image processing articles
written just for the
prosperity and help
for the students searching
for Image Processing
free stuff. |
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