Introduction to Digital Image Processing

1. IT6005 - Digital Image
Processing
K.Ragavan
Assistant Professor (SG)
Department of ECE
Ramco Institute of Technology
Rajapalayam
Regulation – 2013
Academic Year (2016-17)

2. Syllabus
IT6005- Digital Image Processing
UNIT I : DIGITAL IMAGE FUNDAMENTALS
UNIT II : IMAGE ENHANCEMENT
UNIT III : IMAGE RESTORATION & SEGMENTATION
UNIT IV : WAVELETS AND IMAGE COMPRESSION
UNIT V : IMAGE REPRESENTATION AND RECOGNITION

3. UNIT I
DIGITAL IMAGE FUNDAMENTALS
pixel
Gray level
Original picture Digital image
f(x, y) I[i, j] or I[x, y]
x
y

4. UNIT I
DIGITAL IMAGE FUNDAMENTALS
• Introduction
• Origin
• Steps in Digital Image Processing Components
• Elements of Visual Perception
• Image Sensing and Acquisition
• Image Sampling and Quantization
• Relationships between pixels
• color models.

5. Introduction
“One Picture is worth more than Ten
Thousand words”

6. Two principal application areas:
• Improvement of pictorial information for human
interpretation
• Processing of image data for storage, transmission and
representation (feature extraction) for autonomous
machine perception

7. Image Processing Fields
• Computer Graphics:
The creation of images
• Image Processing:
Enhancement or other manipulation of the image
• Computer Vision:
Analysis of the image content

8. Image Processing Fields
Input / Output Image Description
Image Image Processing Computer Vision
Description Computer Graphics AI
Image Processing is defined as “a discipline in which both the
input and output of a process are images”
But, according to this classification, trivial tasks of computing the
average intensity of an image would not be considered an image
processing operation

9. • What is an Image?
 Picture, photograph
 Visual data
 Usually two or three dimensional
• What is a digital image?
 An image which is “discretized,”, i.e., defined on a discrete grid
 Two-dimensional collection of light values (or gray values)

10. Define Image
• An image is defined as a two dimentional function, f(x,y) that
carries some information, where x and y are known as spatial or
plane coordinates.
• The amplitude of ‘f’ at any pair of coordinates (x,y) is called the
intensity or gray level of the image at that point.
Analog Image
• An analog image is mathematically represented as a continuous
range of values that give the position and intensity.
Example: camera and film, generally formats or objects we can see.

11. Digital Image:
• A digital image is created through the process of digitization. It is
the representation of a two dimentional image using one or zeros.
All the amplitude values and coordinate values (x,y) are finite in
a digital image
Pixel
• Pixels are small individual elements of a digital image
• These are also known as image elements or pixels or picture
elements
• Each and every pixel has a particular location and brightness or
intensity value
• A finite number of pixels form a digital image

12. Digital Image
• An image can be defined as a two-dimensional function f(x,y)
Where x and y are spatial coordinates
x,y which is called the intensity or gray level of the
image at that point.
f is the amplitude of any pair of coordinate
x,y and f are all finite and discrete quantities.
 An image is composed of a finite number of elements, each of
which has a particular location and value
referred to as picture elements , image element, Pels
(or) pixels

13. Image Processing
• It is defined as the process of analyzing and manipulating images
using a computer
Analog Image Processing
• Any image processing task which is conducted on two-
dimentional analog signals by analog means is known as analog
image processing
Digital image processing
• Digital image processing is the study of representation and
manipulation of pictorial information by a computer.
Processing Digital Images by means of Digital Computers

14. Advantage of DIP
•It allows wide range of algorithms to be applied to the input data
•It avoids noise and signal distortion problems
Examples:
 Enhancing the edges of an image to make it appear sharper
 Remove “noise” from an image
 Remove motion blur from an image
Need for DIP
•To improve the pictorial information for better clarity (human
interpretation)
•To process image data for storage, transmission and representation
for autonomous machine perception

15. IMAGE CLASSIFICATION
Images(2D)
Color image
Full color
(True color of
image)
Pseudo color
Gray scale
image
Binary image

16. Digital Image

17. Digital Image Representation
















)1,1(...)1,1()0,1(
............
)1,1(......)0,1(
)1,0(...)1,0()0,0(
),(
MNfNfNf
Mff
Mfff
yxf
Digital Image Image Elements
(Pixels)
12/4/2013 17

18. Digital Image Representation contd…
• Image definition:
– A 2D function obtained by sensing a scene
– f(x,y)
f - intensity, grey level
x,y - spatial co-ordinates
• No. of grey levels, L = 2B
• B = no. of bits
B L Description
1 2 Binary Image (black and white)
6 64 64 levels, limit of human visual system
8 256 Typical grey level resolution
f(N-1,M-1)
f(o,o)
N
M

19. Common image file formats
• GIF (Graphic Interchange Format)
• PNG (Portable Network Graphics)
• JPEG (Joint Photographic Experts Group)
• TIFF (Tagged Image File Format)
• PGM (Portable Gray Map)
• FITS (Flexible Image Transport System)

20. Image Representation
12/4/2013 20

21. Pixel Notation
12/4/2013 21

22. • Number of pixel in the digital image = number of rows x number
of columns.
• The number of bits required to encode the pixel value is called bit
depth.
Bit Depth is a power of two
• In grey scale, 8 bits are used to represent the shades between
0-255. So bit depth of grey scale is 8
• Set of all colors that can be represented by bit depth is called
gamut or palette
• No. of bits used to represent an image = no. of rows x no. of
columns x bit depth
12/4/2013 22

23. RESOLUTION
• how small and densely packed the pixels are. It tells you how
many pixels you have per inch.
• The higher the resolution, the sharper the images look, to a
point.
• Monitors only have about 72 screen pixels per inch.
• Most web graphics have a resolution of 72 pixels per inch.
12/4/2013 23

24. 18 ppi
36 ppi
72 ppi
12/4/2013 24

25. Image Representation (Gray/Color)
• A gray level image is usually represented by an M by N matrix
whose elements are all integers in {0,1, …, 255}
corresponding to brightness scales
• A color image is usually represented by 3 M x N matrices
whose elements are all integers in {0,1, …, 255}
corresponding to 3 primary primitives of colors such as Red,
Green, Blue

26. Images

27. Gray Scales
• Low intensity-
Lower gray scale
value
• Highest possible
gray level=2n-1

28. Pixels in a Gray Level Image

29. Gray and Color Image Data
• 0, 64, 144, 196, 225, 169, 100, 36
• (R, G, B) for a color pixel
Red – (255, 0, 0)
Green – ( 0, 255, 0)
Blue – ( 0, 0, 255)
Cyan – ( 0,255, 255)
Magenta – (255, 0, 255)
Yellow – (255, 255, 0)
Gray – (128, 128, 128)

30. History/The Origin of DIP
• Early 1920s: One of the first applications of digital imaging was
in the newspaper industry
– The Bartlane cable picture
transmission service
– Images were transferred by
submarine cable between
London and New York
– Pictures were coded for cable
transfer and reconstructed at the
receiving end on a telegraph printer

31. •Mid to late 1920s: Improvements to the Bartlane system resulted in
higher quality images
– New reproduction processes based on photographic techniques
– Increased number of tones in reproduced images
Improved Digital Image Early 15 tone Digital image

32. Development of Digital Computer
• The actual digital image processing started with the invention of
digital computers because they require more storage space and
computational power
• Although the basic idea of developing computer started more
than 5000 years ago with abacus, the modern digital computers
were started developing in 1940s, with the introduction of below
mentioned von neumann concepts:
• A memory to hold stored program and data
• Conditional branching
• These two are the basic ideas of CPU

33. • 1948 – invention of transistors at BELL laboratories
• 1950s-1960s – development of high level programming
languages: COBOL, FORTRAN
• 1958s – invention of integrated circuit (IC) at Texas Instruments
• 1960s – development of Operating Systems(OS)
• 1970s – development of microprocessor by Intel
- miniaturization of components started with Large Scale
Integration (LSI)
• 1981 – Introduction of personal computer by IBM
• 1980s – Very Large Scale Integration (VLSI) started and
developed to ultra Large Scale Integration (ULSI)

34. •1960s: Improvements in computer technology for improving images
from a space probe began at the Jet Propulsion Laboratory, California
and the onset of the space race led to a surge of work in digital image
processing
– 1964: Computers used to
improve the quality of
images of the moon taken
by the Ranger 7 probe
– Such techniques were used
in other space missions
including the Apollo landings
A 1st picture of the moon taken by U.S
spacecraft Ranger 7 probe on July
31,1964 about 17 minutes before
landing

35. •1970s: Digital image processing begins to be used in medical
applications
– 1979: Sir Godfrey N.
Hounsfield & Prof. Allan M.
Cormack share the Nobel
Prize in medicine for the
invention of tomography,
the technology behind
Computerised Axial
Tomography (CAT) scans
Typical head slice CAT image

36. Computer Tomography
• Sir Godfrey N. Housefield and Prof. Allan M. Cormack
• shared 1979 Nobel Prize in Medicine for the invention of CT

37. X-Ray Imaging

38. Gamma-Ray Imaging

39. •1980s - Today: The use of digital image processing techniques
has exploded and they are now used for all kinds of tasks in all
kinds of areas
– Image enhancement/restoration
– Artistic effects
– Medical visualisation
– Industrial inspection
– Law enforcement
– Human computer interfaces

40. Applications of DIP

41. • Agricultural (Fruit grading, harvest control, seeding, fruit picking ...)
• Communications (compression, video conferencing,...)
• Character recognition (printed and handwritten)
• Commercial (Bar code reading, bank cheques, signature, ...)
• Document processing (electronic circuits, mechanical drawings,
music, ...)
• Human (Heads and faces, hands, body, ...)
• Industrial (Inspection, part pose estimation and recognition, control,...)
• Leisure and entertainment (museums, film industry, photography,...)
• Military (tracking, detection, ...)
• Police (fingerprints, surveillance, DNA analysis, biometry, ...)
• Traffic and transport (Road, airport, seaport, license identification, ...)

42. Sequence of Digital Image Processing

43. • A digital image is an array of real or complex numbers represented
by a finite number of bits.
• An image given in the form of transparency slide, photograph (or)
chart is first digitized and stored as a matrix of binary digits in
computer memory.
• This digitized image can then be processed and/or displayed on a
high resolution television monitor.
• For display, the image is stored in a rapid access buffer memory
which refreshes the monitor at 30 frames/sec to produce a visibly
continuous display.
• Mini (or) Micro computers are used to communicate and control
all the digitization, storage, processing and display operation via a
complex network.

44. Steps in Digital Image Processing
Image
Acquisition
Image
Restoration
Morphological
Processing
Segmentation
Object
Recognition
Image
Enhancement Representation
& Description
Problem Domain
Colour Image
Processing
Image
Compression
Wavelets &
Multiresolution
processing
Knowledge Base
Outputs of these processes generally are images
Outputsoftheseprocessesgenerallyareimageattributes
manipulating an image
Improving the appearance
representing images in various degrees of
resolution
reducing the storage representation and
description of shape
partition an image
assigns label

45. Fundamental Steps in DIP
Step 1: Image Acquisition
• To acquire a digital image
• Generally, the image acquisition stage involves pre-processing
such as scaling
• It requires an imaging sensor and the capability to digitize the
signal (or) image produced by the sensor
• The sensor should be monochrome (or) color TV camera that
produces an entire image of the problem domain every 1/30
seconds.
Briefly,
• The image is captured by a sensor (eg. Camera), and digitized if
the output of the camera or sensor is not already in digital form,
using analogue-to-digital convertor

46. Fundamental Steps in DIP
Step 2: Image Enhancement
The process of manipulating an image so that the result is more
suitable than the original for specific applications.
The idea behind enhancement techniques is to bring out details
that are hidden, or simple to highlight certain features of interest
in an image.

47. Fundamental Steps in DIP
Step 3: Image Restoration
– Improving the appearance of an image
– It is the objective process
Tend to be mathematical or probabilistic models.
Filters are used to restore the original images.

48. Fundamental Steps in DIP
Step 4: Colour Image Processing
• Use the colour of the image to extract features of interest in an
image
• It is an area that has been gaining in importance because of the
significant increase in the use of digital images over the internet
Step 5: Wavelets
• Wavelets are the foundation of representing images in various
degrees of resolution.
• It is used for (i) image data compression.
(ii) pyramidal representation – it is the process of
subdividing images successively into smaller
regions

49. Fundamental Steps in DIP
Step 6: Compression
• It deals with Techniques for reducing the storage required to
save an image or the bandwidth required to transmit it.
Step 7: Morphological Processing
• It deals with Tools for extracting image components.
• These components will be useful in the representation and
description of shape.

50. Fundamental Steps in DIP
Step 8: Image Segmentation
• Segmentation procedures partition an image into its constituent
parts or objects.
• This step divides the image into many sub regions and extracts
the regions that are necessary for further analysis.
• The portion of image that are not necessary, such as image
backgrounds (dictated by the imaging requirement) are
discarded
Important Tip: The more accurate the segmentation, the
more likely recognition is to succeed.

51. Fundamental Steps in DIP
Step 9: Representation and Description
- Representation: Make a decision whether the data should be
represented as a boundary or as a complete region. It is almost
always follows the output of a segmentation stage, which usually
is raw pixel data
- Boundary Representation: Focus on external shape
characteristics, such as corners
- Region Representation: Focus on internal properties, such
as texture or skeleton shape

52. Fundamental Steps in DIP
Step 9: Representation and Description
- Choosing a representation is only part of the solution for
transforming raw data into a form suitable for subsequent
computer processing (mainly recognition)
Description: also called, feature selection, deals with extracting
attributes that result in some information of interest.
(or) basic for differentiating one class of object from another

53. Fundamental Steps in DIP
Step 9: Recognition and Interpretation
Recognition: the process that assigns label to an object
based on the information provided by its description.
Step 10: Knowledge Base
• Knowledge about a problem domain is coded into an image
processing system in the form of a knowledge database.
• It can be defined as software that may help to user
• Knowledge may be as simple as detailing regions of an image
where the information of interest is known to be located, thus
limiting the search that has to be conducted in seeking that
information

55. References:
• Rafael C. Gonzales, Richard E. Woods, “Digital Image
Processing”, Third Edition, Pearson Education, 2010.
• S.Jayaraman,S.Esakkirajan and T.Veerakumar, “Digital Image
Processing”, Tata McGraw Hill Education Pvt.Ltd,2012.