The document provides information about a seminar presentation on digital image processing. It discusses the following key points: - The presentation was given by two students and covered topics like the introduction, history, functional categories, steps, necessity, filtering, technologies, advantages/disadvantages, and applications of digital image processing. - A brief history of digital image processing is provided, noting its origins in newspaper printing and early uses in space applications and medical imaging. - Functional categories of digital image processing include image enhancement, restoration, and information extraction. Key steps involve acquisition, enhancement, restoration, compression, and segmentation. - Technologies discussed include pixelization, component analysis, independent component analysis, hidden Markov models,

1. SHREE DIGAMBER INSTITUTE OF TECHNOLOGY
NEAR BHANDAREJ MOD NH-11,DAUSA

2. A
SEMINAR PRESENTATION
ON
DIGITAL IMAGE
PROCESSING
PRESENTED BY:-
APOORVA VYAS/ECE/7TH SEM/12EDIEC001
KARAN KUMAR JOSHI/ECE/7TH SEM/12EDIEC007

3. CONTENTS
Introduction
History of Image Processing
Functional Categories
Steps in Image Processing
Necessity
Filtering in Image Processing
Technologies
Advantages & Disadvantages
Application
Future Scope of Image Processing

4. INTRODUCTION
DIGITAL IMAGE PROCESSING generally refers to processing of a
2-D picture by a Digital Computer. In other sense it is the
processing of any 2-D data. A Digital image is an array of real
and complex no. represented by finite no of bits.
DIP has its several uses, processes and applications. Here we will
know about them.

5. H
I
S
T
0
R
Y
 Initially, the digital image processing was implemented
newspaper industry. At that time , the first picture was sent
by submarine cable between London and New York. In early
1920 the printing equipment codec picture was transmitted
and was reconstructed at the receiving
 The Bartlane picture transmission system was used to
transport a picture across the Atlantic.
 In 1960s, the computer perform the meaningful and powerful
image processing tasks. It was known as the birth of digital
image processing.
 The first image of moon was taken by Ranger 7 on 31 july
1964 and was used for geometric corrections.
 Alongwith the space applications , digital image processing is
also used in medical imaging , remote earth resources,
observations and astronomy in 1970s.

6. Functional Categories
Image
Enhancement
Information
Extraction
Image Restoration
Functional categories in image
processing

7. IMAGE RESTORATION
f(x, y) g(x, y)
f(x, y)
Noise n(x, y)
Degradation
function
Restoration
filter+
 Image restoration technique is used to improve an image in some sense. This
technique recover an image which has been degraded. So the restorations are
oriented towards modelling the degradation and then applying the inverse
process to recover original image.
 The restoration is obtained an estimated image of the original image. The
above shown model consist of a degradation function with an additive noise
and a restoration filter .
 Here the image f(x, y) degraded by degradation function and the noise is
added so we got the new function g(x, y). Now this is fed to the restoration
filter and we got the estimated image.

8. IMAGE ENHANCEMENT AND INFORMATION
EXTRACTION
 Image Enhancement :
Enhancement is the
modification of an image
to alter its impact on the
viewer
 Information Extraction :
utilize computers to
provide corrected and
improved images for study
by human interpreters.

9. STEPS IN IMAGE
PROCESSING
STEP
S
IMAGE
ENHANC
E-MENT

10. IMAGE ACQUISITIONTV camera) and digitized, if the output of the camera or sensor
is not already
in digital form- an analog-to-digital converter (ADC) digitizes it.
Camera:
Camera consists of 2 parts:
A lens that collects the appropriate type of radiation emitted
from the
object of interest and that forms an image of the real object.
Semiconductor device – so called charged coupled device or
CCD
which converts the irradiance at the image plan into an electrical
signal.
Frame Grabber
Frame Grabber only needs circuits to digitize the electrical
signal (standard

11. IMAGE ENHANCEMENT
 Image Enhancement is the
process of manipulating an
image so that the result is
more suitable than the
original for specific
applications..

12. IMAGE RESTORATION
 Improving the
appearance of the
image. Tend to be
mathematical or
probabilities models
of image degradation.

13. COLOR IMAGE PROCESSING
 The human visual system
can distinguish hundreds
of thousands of different
colour shades and
intensities.
 In an image, a great deal
of extra information may
be contained in the
colour, and this extra
information can then be
used to simplify image
analysis, e.g. object
identification and
extraction based on
colour.
Figure
Figure 1: The visible spectrum.

14. WAVELETS
 The wavelet transform plays an extremely
crucial role in image compression.
 For image compression applications, wavelet
transform is a more suitable technique
compared to the Fourier transform.
 Because the resulting function after Fourier
transform is a function independent of time.
On the other hand, wavelet transforms are
based on wavelets which are varying frequency
in limited duration. Due to the practicality of
the wavelet transforms, this research paper is
written to investigate the properties and the
improvements that can be made to enhance
the performance of the wavelet transforms.

15. COMPRESSION Image compression is
minimizing the size in bytes of a
graphics file without degrading
the quality of the image to an
unacceptable level.
 The reduction in file size allows
more images to be stored in a
given amount of disk or memory
space.
 It also reduces the time required
for images to be sent over the
Internet or downloaded from
Web pages.

16. SEGMENTATION
 Computer tries to separate objects
from the image background.
 It is one of the most difficult tasks in
DIP.
 Segmentation kinds:
Autonomous Segmentation.
Rugged Segmentation (long process to
get successful solution).
Erratic Segmentation.

17. NECESSITY OF IMAGE PROCESSING
 The digital image is
“invisible” .it must be
prepared for viewing on
one or more o/p
devices(laser printer,
monitor etc. )
 The digital image can be
optimized for the
application by enhancing
for altering the
appearances of structures
within it.

18. FILTERING IN IMAGE PROCESSINGThe filtering used to eliminating noise.
This filter performs spatial filtering on each
individual pixel in an image using the grey
level values in a square or rectangular
window surrounding each pixel.
For example:
a1 a2 a3
a4 a5 a6 3x3 filter window
a7 a8 a9
The average filter computes the sum of all
pixels in the filter window and then divides
the sum by the number of pixels in the
filter window:
Filtered pixel = (a1 + a2 + a3 + a4 ... + a9)
/ 9

19. TECHNOLOGIES
USED
TECHs
PIXELIZATION
COMPONENT
ANALYSIS
INDEPENDENT
ANALYSIS
HIDDEN
MARKOV
MODELS
SELF
ORGANIZING
MAPS
NEURAL
NETWORKS
WAVELETS

20. PIXELIZATIONThe result of enlarging a
digital image further than
the resolution of the
monitor device, usually
72dpi (dots per inch),
causing the individual
pixels making up the
image to become more
prominent, thus causing
a grainy appearance in
the image.
Blurring a part of a
picture by grouping pixel
areas.

21. PRINCIPLE COMPONENT ANALYSIS
 Principal component analysis PCA
belongs to linear transforms based on
the statistical techniques.
 This method provides a powerful tool
for data analysis and pattern
recognition which is often used in
signal and image processing.
 As a technique for data compression,
data dimension reduction.
 There are various algorithms based on
multivariate analysis or neural
networks that can perform PCA on a
given data set.
 It introduces PCA as a possible tool in

22. INDEPENDENT COMPONENT ANALYSIS
 Independent component analysis (ICA) is a
statistical and computational technique for
revealing hidden factors that underlie sets of
random variables, measurements, or signals.
 The main concept of ICA applied to images insists
on the idea that each image (subimage) may be
perceived as linear superposition of features ai(x,
y) weighted by coefficients si.
 In case of ICA, features are represented by
columns of mixing matrix ai and si are elements

23. HIDDEN MARKOV MODEL
 A hidden Markov model (HMM) is a statistical Markov
model in which the system being modeled is assumed to
be a Markov process with unobserved (hidden) states.
 An HMM configuration is described for many-dimensional
image processing by several different ways (line-by-line,
series of presenting elements, etc.).
 The applications to the model calculations and binary
image recovery.

24. SELF ORGANIZING MAPS
 In image processing the Self Organizing
Maps are used for Image classification and
retrieval(CBIR) ,group the images in different
classes.
 The SOM (Self Organizing Map) Neural
Network or commonly called a Kohonen
Neural Network system is one of the
unsupervised learning model that will
classify the units by the similarity of a
particular pattern to the area in the same

25. WAVELETS
The wavelet transform plays an extremely crucial role in image
compression.
For image compression applications, wavelet transform is a more
suitable technique
compared to the Fourier transform.
 The resulting function after Fourier transform is a function independent of
time.
 On the other hand, wavelet transforms are based on wavelets which are
varying frequency in limited duration.
 Due to the practicality of the wavelet transforms, this research paper is
written to investigate the properties and the improvements that can be made
to enhance the performance of the wavelet transforms.

26. ADVANTAGES AND
DISADVANTAGES
ADVANTAGES:-
 Digital image processing made digital image can be noise
free .
 It can be made available in any desired format. (X-rays,
photo negatives, improved image, etc)
 Digital imaging is the ability of the operator to post-
process the image .It means manipulate the pixel shades
to correct image density and contrast .
 Images can be stored in the computer memory and easily
retrieved on the same computer screen .
 Digital imaging allows the electronic transmission of
images to third-party providers

27. DISADVANTAGES:-
 The initial cost can be high depending on
the system used .
 If computer is crashes then pics that have
not been printed and filed into Book
Albums that are lost.
 Digital cameras which are used for digital
image processing have some
disadvantages like:
Memory Card Problems
Higher Cost
Battery Consumption

28. APPLICATIONS
Medical
Digital Cinema
Transmission &
coding
Remote
sensing &
Robot vision
Image
processing
architecture
Color processing
Video
processing

29. Medical Field Application
The common applications of DIP in the
field of medical is
Gamma ray imaging
PET scan
X Ray Imaging
Medical CT
UV imaging

30. DIGITAL CINEMA

31. TRANSMISSION AND ENCODING
TRANSMISSION-
 This the process of communication used for the
transmission of images.
 For transmission there are many ways available(internet
,fax ,printer etc.)
ENCODING-
 By the encoding the image is converted in the form which
can be transmitted.

32. REMOTE SENSING AND ROBOT VISION
HURDEL DETECTION
REMOTE SENSING

33. IMAGE PROCESSING
ARCHITECTURE

34. COLOR PROCESSING
Color processing includes
processing of colored
images and different color
spaces that are used. For
example RGB color model,
CMY, HSI. It also involves
transmission, storage,
and encoding of these
color images

35. VIDEO PROCESSING
A video is nothing but just the very fast
movement of pictures. The quality of the
video depends on the number of
frames/pictures per minute and the
quality of each frame being used. Video
processing involves noise reduction, detail
enhancement, motion detection, frame
rate conversion, aspect ratio conversion,
color space conversion etc.

36. FUTURE SCOPE