| Image
Processing Articles
Index |
 |
| Basic
Working Mechanism |
| In
its most straightforward
implementation, this
operator takes as
input two identically
sized images and produces
as output a third
image of the same
size as the first
two, in which the
intensity of each
pixel is the sum of
the values of the
corresponding pixels
from each of the two
input images. More
sophisticated versions
allow more than two
images to be combined
with a single operation. |
|
| A
common variant of
the operator simply
allows a specified
constant to be added
to every pixel. |
|
| 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. |
|
| How
Nature Produces Colors: |
|
| in
fact, the basic colors
present in the nature
are three Red, Green
and blue. The rest
of the colors are
the combination of
these basic colors.
The following figures
illustrate the basic
mechanism performed
by the addition of
these colors. |
|
 |
|
| we
can see here that
how the addition of
colors are changing
them to some other
colors. That is the
basic reason due to
which we say that
we can make 224 =16777216
different colors. |
|
| General
Working |
|
| Q(i,j)=P1(i,j)+
P2(i,j) |
| Or
if it is simply desired
to add a constant
value C to a single
image then: |
| Q(i,j)=P(i,j)+
C |
|
| If
the pixel values in
the input images are
actually vectors rather
than scalar values
then the individual
components (e.g. red,
blue and green components)
are simply added separately
to produce the output
value. |
|
| If
the image format being
used only supports,
say 8-bit integer
pixel values, then
it is very easy for
the result of the
addition to be greater
than the maximum allowed
pixel value. The effect
of this depends upon
the particular implementation.
The overflowing pixel
values might just
be set to the maximum
allowed value, an
effect known as saturation.
Alternatively the
pixel values might
wrap around from zero
again. If the image
format supports pixel
values with a much
larger range, e.g.
32-bit integers or
floating point numbers,
then this problem
does not occur so
much. |
|
| 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 Image Addition,
take the example of
the following images: |
|
  |
|
| the
Resultant Image Obtained
by the addition of
the above images is
as: |
|
 |
|
| Sample
Project |
|
| Please
Review other articles
based on Logical Operators
to get the better
understanding of the
project. The application
seems to be in this
GUI |
|
 |
|
| 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. |
|
Download
Project Files
 |
|
| Image
Processing Articles
Index |
