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Digital image processing
A Seminar Report Submitted In Partial Fulfillment Of The
Requirement
For The Award Of The Degree Of
Bachelor Of Technology
In
Computer science and engineering
By
n.ch.karthik 08U31A105A2
DEPARTMENT OF INFORMATION TECHNOLOGY
BOMMA INSTITUTE OF TECHNOLOGY & SCIENCE
Allipuram, Khammam-500 315
(2011-2012)

DEPARTMENT OF INFORMATION TECHNOLOGY
BOMMA INSTITUTE OF TECHNOLOGY &
SCIENCE
(Approved by AICTE,Govt of A.P & Affiliated to JNTUH)
CERTIFICATE
This is to certify that the seminar report entitled
“digital image processing” that is being submitted by
n.ch.karthik in partial fulfillment for the award of B.Tech in
computer science Technology to the Jawaharlal Nehru
Technological University is a record of bonafide work carried
out by his.
HEAD OF THE DEPARTMENT
PRINCIPAL
2

ACKNOWLEDGEMENT
First and foremost, I express my sincere thanks to Mr.shiva
Prasad, Head Of The Department, Dept of
computerscience&Technology, BITS who stood as silent
inspiration behind this seminar. My heartfelt thanks for this
endorsement and valuable suggestions.
Secondly, I wish to express my profound thanks to Prof
S.MURALIKRISHNA, Principal, Bomma Institute Of
Technology & Science, Khammam for providing necessary
facilities to make this seminar a success.
I thank all the members of teaching and non-teaching staff
members, also who have assisted us directly or indirectly for
successful completion of this seminar.
Finally, I would like to express my sincere gratitude to our
parents who
are constantly encouraging us throughout our life and completion
of this seminar.
3

contents
• Abstract
• Introduction
• Correcting the raw image
• Plotting image
• Histogram, equalization
• Mathematical manipulations
• References
• Conclusion
4

Abstract
Interest in digital image processing methods stems from two
principal application areas: improvement pictorial information for
human interpretation; and processing of image data for storage,
transmission, and representation for autonomous machine perception.
Digital image processing refers to processing digital images by
means of a digital computer. Note that digital image is composed of
finite number of elements, each of which has a particular location and
value.
There will be disturbances in every aspect digital image
processing has no exception. Digital image processing also suffers from
some sort of disturbances from external sources and others. Which is
discussed in this aspect.
As we know that the images could be in the form of analog
signals there is a need to convert these signals to digital form which can
be done by plotting the image using different transfer functions which is
explained here under. A transfer function maps the pixel values from
the CCD to the available brightness values in the imaging software all
the images so far have been plotted using linear transfer functions
Filter masks and other manipulations have also discussed in this
aspect in order to make the image filter and get a clear cut form of the
image.
5

INTRODUCTION
What is digital image processing
A digital image is picture which is divided into a grid of “pixels”
(picture elements). Each pixel is defined by three numbers(x, y, z), and
displayed on a computer screen
The first two numbers give the x and y coordinates of the pixel,
and the third gives its intensity, relative to all the other pixels in the
image. The intensity is a relative measure of the number of
photons collected at that photosite on the CCD, relative to all the others,
for that exposure.
The clarity of a digital image depends on the number of “bits” the
computer uses to represent each pixel. The most common type of
representation in popular usage today is the “8-bit image”, in which the
computer uses 8 bits, or 1 byte, to represent each pixel.
This yields 28
or 256 brightness levels available within a given
image. These brightness levels can be used to create a black-and-white
image with shades of gray between black(0) and white(255), or assigned
to relative weights of red, green, and blue values to create a color image.
The range of intensity values in an image also depends on the
way in which a particular CCD handles its ANALOG TO DIGITAL
(A/D) conversion.
12-bit A/D conversion means that each image is capable of
212
(4096) intensity values.
6

If the image processing program only handles 28
(256) brightness
levels, these must be divided among the total range of intensity values in
a given image.
This histogram shows the number of pixels in a 12-bit image that
have the same intensity, from 0 to 4095.
Suppose u have software that only handles 8-bit information you
assign black and white limits, so that all pixels with values to the left of
the lower limit are set to 0, while all those to the right of the upper limit
are set to 255. this allows u to look at details within a given intensity
range.
So… a digital image is a 2-dimensional array of numbers, where
each number represents the amount of light collected at one photosite,
relative to all the other photosites on the CCD chip.
7

It might look something like….
• 98 107 145 126 67 93 154 223 155 180 232 250 242 207 201
• 72 159 159 131 76 99 245 211 165 219 222 181 161 144 131
• 157 138 97 106 55 131 245 202 167 217 173 127 126 136 129
• 156 110 114 91 70 128 321 296 208 193 191 145 422 135 138
by this we can guess the brightest pixel in the
“image”…
CORRECTING THE RAW IMAGE
Every intensity value contains both signal and noise. Your job is to
extract the signal and eliminate the noise!
But before that the sources of noise should be known. They can
be
a) The Dark Current:
Since electrons in motion through a metal or semiconductor create a
current, these thermally agitated electrons are called the Dark Current.
…so, to eliminate thermal electrons, the CCD must be COOLED
as much as possible. The cooler one can make one’s CCD, the less
dark current one will generate. In fact, the dark current decreases
roughly by a factor of 2 for every 70
c drop in temperature of the chip.
At – 1000
c the dark current is negligible.
When you process an image correctly, you must account for this
dark current, and subtract it out from the image. This is done by taking a
“closed shutter” image of a dark background, and then subtracting this
dark image from the “raw” image you are observing.
The exposure time of the dark image should match that of the
image of the object or starfield you are viewing.
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In fact, the one who regularly take CCD image keep files of dark
current exposures that match typical exposure times of images they are
likely to take, such as: 10,20,45,60or 300 seconds, which are updated
regularly, if not nightly.
b) The Bias Correction
CCD cameras typically add a bias value to each image they record. If
you know that the same specific bias value has been added to each
pixel, you can correct for this by subtracting a constant from your sky
image.
c) Pixel – to – pixel Sensitivity variation
Another source of noise is the inherent variation in the response of each
pixel to incident radiation. Ideally, if your CCD is functioning properly,
there should be no variation in pixel value when you measure a
uniformly–illuminated background. However, nothing I perfect, and
there usually is some slight variation in the sensitivity of each photosite,
even if the incident radiation is totally uniform.
This can be accounted for by taking a picture of a uniformly bright field,
and dividing the ray image by this “flat” field – a process called flat
fielding. The length of time to expose the flat image should be enough
to saturate the pixels to the 50% or 75% level.
One must take 4 pictures before beginning to process the image. One
needs 4 images to create a “noiseless” image of the sky.
1) The original;
2) A dark exposure of the same integration time as
your original;
3) A flat exposure;
4) And another dark exposure, of the same
integration time as your flat exposure!
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Final image = raw image - dark raw
(Flat - dark flat)
(don’t forget to subtract your dias
correction form each image)
so… the correct raw image taken with any CCD one must
• Subtract the bias correction form each image;
• Subtract the dark current image from the raw image;
• Subtract the dark current image form the flat field
image;
• Divide the dark – subtracted raw image by the dark-
subtracted flat image.
3 ways of displaying your image to Enhance Certain Features
Once you have dark – subtracted and flat – fielded your image, there are
many techniques you can use to enhance your signal, once you have
eliminated the noice.
These manipulations fall into two basic categories:
1) CHANGING THE WAY IN WHICH THE INFORMATION
IS PLOTTED ON YOUR SCREEN
These methods are basically mapping routines, and include:
• Limiting the visualization thresholds within the
histogram
• Plotting the image using different transfer functions
• Histogram equalization
2) MATHEMATICAL METHODS OF MASSAGING YOUR
DATA
These methods employ various matrix multiplications, Fourier
transformations, and convolutions, and we will address them in the next
section.
Limiting the visualization thresholds
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within the histogram
we already saw that the histogram function shows you the distribution
of brightness values in an image, and the number of pixels within the
same brightness value.
In the histogram shown, most of the useful information is contained
between the user-defined limits.
The peak of intensities on the lower end could possibly be some faint
feature, which could be enhanced in a variety of ways…
By changing the visualization limits in the histogram, the user can
pre – define the black and white levels of the image, thus increasing the
level of detail available in the mid – ranges of the intensities in a given
image.
11

Some examples of histogram limitation is used to examine
different features
Plotting the image using different transfer
functions.
A transfer function maps the pixel values from the CCD to the
available brightness values in the imaging software all the images so far
have been plotted using linear transfer functions
…but you can also use non – linear scaling
Human eyes see a wide range of intensities because our vision
LONGARITHMICALLY scaled.
When you plot digital images logarithmically, it allows you to see a
broader range of intensities, and can give a more “natural” look… as if
you could see the object with your naked eyes.
…or, you could use a Power Law scaling
Fractional powers enhance low intensity features, while powers greater
than 1 enhance high intensity features.
12

HISTOGRAM EQUILIZATION
…a means of flattening your histogram by putting equal numbers of
pixels in each “bin”, it serves to enhance mid – range features in an
image with a wide range of intensity values.
When you equalize your histogram, you distribute your 4096
intensity from your CCD equally among the intensity values available in
your software. This can be particularly useful for bringing out features
that close to the sky background, which would otherwise be lost.
13

After you have corrected your raw image so that
you are confident that what you are seeing really
comes form incident photons and not electronic
noise of your CCD. You may still have
unwanted components in your image.
It is time now to perform mathematical
operations on your signal which will enhance
certain features, remove unwanted noise, smooth
rough edges, or emphasize certain boundaries.
...and this brings us to our last topic for
this module:
Filters masks and other
Mathematical Manipulations.
The rhyme and reason
14

Basically, any signal contains information
of varying frequencies and phases. In digital
signal enhancement, we attempt to accentuate
the components of that signal which carry the
information we want, and reduce to
insignificance those components which carry the
noise.
Audio equipment, such as your stereo or CD
player, has filters which do this for one –
dimensional, time – varying audio signals.
In digital image analysis we extend these
techniques to 2 – dimensional signals which are
spatially varying.
In any case, the basic idea is the same:
Get the most out of your data
For the least amount of hassle!
Here’s How it Works:
you create an “n X n” matrix of numbers, such
as 3 X 3 or 5 X 5, and you move this across your
image, like a little moving window, starting at
the upper left corner (that’s 0,0,recall).
You “matrix multiply” this with the pixel values
in the image directly below it to get a new value
for the center pixel.
You move the window across your image, one
pixel at a time, and repeat the operation, until
you have changed the appearance of the entire
image.
Here’s an example of one kind of filter:
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1 2 1
2 4 2
1 2 1
if you move this matrix across an image and
matrix multiply along, you will end up replacing
the center pixel in the window with the weighted
average intensity of all the points located inside
the window.
HERE’S HOW TO DO IT:
I11 I12 I13 I14
I15…
I21 I22 I23 I24 I25
…
I31 I32 I33 I34 I35
…
I41 I42 I43 I44 I45
…
I51 I52 I53 I54 I55…
You plop this window
down over a 3 X 3 section
of your image, do a matrix
multiplication on the center
pixel, I22 in this example,
and the new value for I22
which is returned is
1I11 + 2I12 + 1I13 + 2I21 +
4I22 + 2I23 + 1I31 + 2I32 + 1I33
I22’=
( 1+2+1+2+4+2+1
+2+1 OR 16)
Imagine your image in 3 dimensions, where the
intensity is plotted as height. Large scale
features will appear as hills and valleys, while
small bright objects like stars will appear as
sharp spikes. Some features will have steep
gradients, while others shallow. Some features
16

may have jagged edges, while others are
smooth.
You can design little n X n windows to
investigate and enhance these kinds of features
of your image in the slides that follow, we will
show examples of low Pass, high pass ,edge
detection, gradient detection, sharpening,
blurring, bias filtering your image. Low – pass
filters enhance the larger scale feature in your
image
High pass filters enhance the short period
features in your image, giving it a sharper look.
Some examples of high pass filters:
0 -1 0 0 -1 0 -1 -1 -1
-1 -1 -1
17

-1 20 -1 -1 10 -1 -1 10 -1
-1 16 -1
0 -1 0 0 -1 0 -1 -1 -1
-1 -1 -1
Edge detection filters are used to locate the
boundaries between regions of different
intensities.
The “bias filter” makes an image look like a bas
relief with shadows. This can be useful for
examining certain details
You can also combine process – such as low
pass, high pass, and image subtraction in a
process called UNSHARP MASKING.
Un sharp masking consists of a 3 – step process:
Make a copy of the image where each pixel is
the average of the group of pixels surrounding it,
so that the large features are not disturbed, but
the small ones are blurred( this is the un sharp
mask).
• The pixel values of the original image are
multiplicated by a constant(“A”), and then
the pixel values of the un sharp mask are
subtracted from this one or more times(“B”).
in this way, the large features are not
changed by much, but the small ones are
enhanced.
• Finally a low – pass filter is applied to the
result.
you can also create your own filter to smooth
or otherwise operate on your image.
18

REFERENCES
Bibliography:
1.Digital image processing by
Rafael C. Gonzalez and
Richard E. Woods
2.www.physics.ucsb.edu
19

Conclusion:
Dip uses it gives effective images It is
used to edits image user wants type..It
is used in satellites ,medical, movies
e.t.c.Colour images styles, animation so
on…User understand anything easy
way. Land cover analysis (water, roads,
20

cities and cultivation), vegetation
features (water content and
temperature) and crop yield analysis; 3-
D terrain rendering from satellite or
aircraft data (road and dam planning);
fire and smoke detection. Space
exploration and Astronomy satellite
navigation and altitude control using
star positions. Video and Film Special
Effects: Animation, and special effects
(Star Wars). Image processing is used
for two somewhat different purposes:
improving the visual appearance of
images (pictorial information) to a
human viewer, and preparing
(processing) images for measurement
of the features and structures present.
The techniques that are appropriate for
each of these tasks are not always the
same, but there is considerable overlap.
This course covers methods that are
used for both purposes.
21

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