| Image
Processing Articles
Index |
 |
| Most
scanned images contains
noise in form of darker
dots and disturbances
caused by the scanning
process. |
|
If
these are not removed
before the feature
extraction and classification,
the image may mistakenly
be interpreted wrong.
Salt and Pepper noise
removal does just
what we desire; it
erases the black dots,
called the Pepper,
and it also fills
in holes in the image,
called Salt. |
|
| The
method is based on
two basic binary image
morphological operations:
Dilations and Erosions
that are based on
the Minkowski addition
and subtraction. |
|
Dilations:
For a binary image
 ,
and 
the dilation of image
A by B is defined
as 
|
|
Erosions:
For a binary image
,
and
the erosion of image
A by B is defined
as  ,
where a+b are vector
addition, 
and  ;
i.e B* denotes the
reflection of B across
the origin 
and A' denotes the
complement of A. |
|
| This
may be a bit tricky
to understand so here
is an other way to
describe it: |
|
Dilation
2: 
Erosion 2:  |
|
To
simplify it even more
we can from the first
definitions define
and obtain the relations:
:equation 1
:equation 2
Where  |
|
| Suppose
that B contains the
origin 0, then
the equation 1 says
that A+B is
the set of all points
p such that the translate
of B by the
vector p intersects
A. The figures
below will perhaps
clarify it: |
|
| Suppose
that B contains the
origin 0, then
the equation 1 says
that A+B is the set
of all points p such
that the translate
of B by the
vector p intersects
A. The figures
below will perhaps
clarify it: |
|
 |
|
 |
|
| The
set A-B consists
of all points p such
that the translate
of B by the
vector p is
completely contained
inside A. This
is illustrated like
this: |
|
 |
|
With
these two operations,
the dilation and erosion,
we can define two
other operations,
the open and close
operation.
The open operation
is an erosion followed
by a dilation and
as defined as follows: .
The close operation
is a dilation followed
by a erosion:  . |
|
Opening
an image will eliminate
small islands, sharp
peaks and thin lines.
Closing an image will
fuse narrow breaks,
close small holes
and smooth contours.
If we then do this
in the right order
we will get great
results. First perform
an opening of A by
B, this will remove
all the black dots
(the pepper), then
perform a closing
on A by B and all
holes will be filled
(the salt). For example,
if we have a scanned
image, A, that look
like this: |
|
 |
|
| and
a structuing element
B: |
|
 |
|
| Then
with the Salt and
Pepper removing we
will end up with something
like this: |
|
 |
|
|
Area: |
| Area
is the total # of
On pixels in the binary
image. As we have
discussed about the
binary image and what
the pixels and their
intensity values.
A pixel in the binary
image has one of two
values i.e., either
1 or 0 where 1 demonstrates
a white pixel and
0 for the black one.
Now area in an image
is the total nomber
of On pixels in the
image. We browse through
the whole image and
calculate the total
number of pixels in
the image. |
|
| C#
Sample Program: |
|
| Guidelines
for Use |
| To
illustrate Conversion
of Color Image to
Grayscale image, we
start with a simple
image containing some
distinct artificial
objects(specifically
text) |
|
| Now
we apply Grayscale
conversion to the
image to convert it
to Grayscale image. |
|
 |
|
| Now
we apply Grayscale
conversion to the
image to convert it
to Grayscale image. |
|
 |
|
| Now
we apply Binary conversion
to the image to convert
it to Binary image. |
|
 |
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