Digital Image Processing_ ch1 introduction-2003

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Digital Image Processing:
Introduction
Dr Ahmad Hassanat

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Introduction

“One picture is worth more than ten
thousand words”
Anonymous

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References
“Digital Image Processing”, Rafael C.
Gonzalez & Richard E. Woods,
Addison-Wesley, 2002
– Support reference

“Machine Vision: Automated Visual
Inspection and Robot Vision”, David
Vernon, Prentice Hall, 1991
– Available online at:
homepages.inf.ed.ac.uk/rbf/BOOKS/VERNON/
– Google.com

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Contents
This lecture will cover:
– What is a digital image?
– What is digital image processing?
– History of digital image processing
– State of the art examples of digital image
processing
– Key stages in digital image processing

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What is a Digital Image?
A digital image is a representation of a twodimensional image as a finite set of digital
values, called picture elements or pixels

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What is a Digital Image? (cont…)
Pixel values typically represent gray levels,
colours, heights, etc
Remember digitization implies that a digital
image is an approximation of a real scene
1 pixel

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What is a Digital Image? (cont…)
Common image formats include:
– 1 sample per point (B&W or Grayscale)
– 3 samples per point (Red, Green, and Blue)
– 4 samples per point (Red, Green, Blue, and “Alpha”,
a.k.a. Opacity)

For most of this course we will focus on grey-scale
images

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What is Digital Image Processing?
Digital image processing focuses on two
major tasks
– Improvement of pictorial information for
human interpretation
– Processing of image data for storage,
transmission and representation for
autonomous machine perception

Some argument about where image
processing ends and fields such as image
analysis and computer vision start

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What is DIP? (cont…)
‫التواصل‬

The continuum from image processing to
computer vision can be broken up into low-,
mid- and high-level processes
Low Level Process

Mid Level Process

High Level Process

Input: Image
Output: Image

Input: Image
Output: Attributes

Input: Attributes
Output: Understanding

Examples: Noise
removal, image
sharpening

Examples: Object
recognition,
segmentation

Examples: Scene
understanding,
autonomous navigation

In this course we will
stop here

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History of Digital Image Processing
Early 1920s: One of the first applications of
digital imaging was in the newspaper industry
– The Bartlane cable picture
Early digital image
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

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History of DIP (cont…)
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

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1960s: Improvements in computing
technology 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 picture of the moon taken
by the Ranger 7 probe
minutes before landing

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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

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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

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Examples: Image Enhancement
One of the most common uses of DIP
techniques: improve quality, remove noise
etc

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Examples: The Hubble Telescope
Launched in 1990 the Hubble
telescope can take images of
very distant objects
However, an incorrect mirror
made many of Hubble’s
images useless
Image processing
techniques were
used to fix this

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Examples: Artistic Effects
Artistic effects are
used to make
images more
visually appealing,
to add special
effects and to make
composite images

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Examples: Medicine
Take slice from MRI scan of canine heart,
and find boundaries between types of tissue
– Image with gray levels representing tissue
density
– Use a suitable filter to highlight edges

Original MRI Image of a Dog Heart

Edge Detection Image

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Examples: GIS
Geographic Information Systems
– Digital image processing techniques are used
extensively to manipulate satellite imagery
– Terrain classification
– Meteorology

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Examples: GIS (cont…)
Night-Time Lights of
the World data set
– Global inventory of
human settlement
– Not hard to imagine
the kind of analysis
that might be done
using this data

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Examples: Industrial Inspection
Human operators are
expensive, slow and
unreliable
Make machines do the
job instead
Industrial vision systems
are used in all kinds of
industries
Can we trust them?

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Examples: PCB Inspection
Printed Circuit Board (PCB) inspection
– Machine inspection is used to determine that
all components are present and that all solder
joints are acceptable solder joints: ‫وصل اللحام‬
– Both conventional imaging and x-ray imaging
are used

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Examples: Law Enforcement
Image processing
techniques are used
extensively by law
enforcers
– Number plate
recognition for speed
cameras/automated
toll systems
– Fingerprint recognition
– Enhancement of
CCTV images

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Examples: HCI

Try to make human
computer interfaces more
natural
– Face recognition
– Gesture recognition

These tasks can be
extremely difficult

‫التعرف على‬
‫الميماءات‬

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Key Stages in Digital Image Processing
Image
Restoration
‫ترميم الصورة‬

Morphological
Processing

Image ‫تحسين الصوره‬
Enhancement

Segmentation

Image ‫الحصول على‬
Acquisition ‫الصوره‬

Object
Recognition

Representation
& Description

Problem Domain
Colour Image
Processing

Image
Compression

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Key Stages in Digital Image Processing:
Image Aquisition
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Image Enhancement
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Image Restoration
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Image Restoration - Examples

Distorted image

Geometrically distorted image

Restored image

Restored image

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Image Restoration – De-noising
Noisy images

Restored “Clean” images

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Morphological Processing
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Segmentation
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Object Recognition
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Representation & Description
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Image Compression
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Image Compression

JPEG Compression)

Original image

JPEG2000 Compression

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Colour Image Processing
Image
Restoration

Morphological
Processing

Image
Enhancement

Segmentation

Image
Acquisition

Object
Recognition

Problem Domain

Representation
& Description

Colour Image
Processing

Image
Compression

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Digitising an image
To convert the continuous function f(x,y) to digital form we need to
sample the continuous sensed data in both coordinates and in
amplitude using finite and discrete sets of values.
– Digitizing the coordinate values is called sampling.
– Digitizing the amplitude values is called quantisation.
The number of selected values in the sampling process is known as
the image spatial resolution. This is simply the number of pixels
relative to the given image area
The number of selected values in the quantisation process is called
the grey-level (colour level) resolution. This is expressed in terms
of the number of bits allocated to the colour levels.
The quality of a digitised image depends the resolution parameters
on both processes.

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Digital image Representaion – Revised
A monochrome digital image is a 2-dimensional light intensity
function f (x,y) whose independent variables (x,y) are digitised
through spatial sampling, and whose intensity values are
quantised by a finite uniformly spread grey-levels. i.e. an image f
can be represented as a 2-dimentional array:
f(1,1)

f(1,3)

…

f(1,n)

f(2,1)

f(2,2)

f(2,3)

…

f(2,n)

f(3,1)

f(3,2)

f(3,3)

…

f(3,n)

:
:

f=

f(1,2)

:
:

:
:

:
:

:
:

f(m,2)

f(m,3)

…

f(m,n)

f(m,1)

Usually, m=n and the number of graylevels are g=2k for some k. The
spatial resolution is mn and g is the greylevel resolution.
RGB based colour images are represented similarly except that f(i,j)
is a 3D vector representing intensity of the three primary colors at
the (i,j) pixel posiotion,

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Spatial Resolution
The spatial resolution of a digital image reflects the amount
of details that one can see in the image (i.e. the ratio of
pixel “area” to the area of the image display).
If an image is spatially sampled at mxn pixels, then the
larger mn the finer the observed details.
For a fixed image area, the noticeable image quality is
directly proportional to the value of mn results.
Reduced spatial resolution, within the same area, may
result in what is known as Checkerboard pattern.
However beyond a certain fine spatial resolution, the
human eye may not be able to notice improved quality.

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Spatial Resolution Vs Image Quality
Decreasing spatial resolution reduces image quality proportionally Checkerboard pattern.

† Images extracted from DIP, 2nd Edition, Gonzalez & Woods, PH.

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Spatial Resolution Vs Image Quality - continued

The checkerboard effect is not visible if a lower–resolution
image is displayed in a proportionately small window.

Effect of grey level resolution

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123
137
Image f = 151
205
250

162
157
155
101
50

200
165
152
100
75
8 bits

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232
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193
88

93
189
130
115
100

f(i,j)← int(f(i,j)/2)

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62

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25
12

50
41
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25
18
6 bits

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21

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15
17
18
25
31

20
19
19
12
6

3
4
4
6
7

5
4
4
3
1

6
5
4
3
2
3 bits

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7
4
6
2

2
5
4
3
3

1
2
2
3
3

2
2
2
1
0

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19
12
9
5 bits

3
2
2
1
1
2 bits

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75
102
125

80
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77
50
25

100
73
82
116
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70
50
96
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7 bits

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24
10

11
23
16
14
12

7
8
9
12
15

10
9
9
6
3

12
9
10
14
9
8
6
12
4
5
4 bits

2
3
2
3
1

1
2
2
1
1

0
1
1
1
1

1
1
1
0
0

1
1
1
0
0
1 bits

1
1
1
1
0

Original image f is reasonably bright, but gradually the pixels get
darker as the Grey-level resolution decreases.

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65
57
50

5
11
8
7
6

0
1
1
0
0

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Effect of grey level resolution

8 bits

5 bits

2 bits

7 bits

6 bits

4 bits

3 bits

1 bit

0 bits !!!

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Zooming and Resizing
It is the scaling of an image area A of wxh pixels by a factor s while
maintaing spatial resolution (i.e. output has sw×sh pixels).
First we need a linear scaling function S to map the coordinates of
new pixels onto the original pixel grid of A.
For each (x,y) in the resized area, we need to interpolate the gray
value sf(x,y) in terms of the pixels values in A that neighbour the
point S(x,y). Different models of approximations are used.

S(A)

A

Example: Scaling A by a factor s=1.5

Zooming and Resizing - Continued

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Interpolation schemes include:
– Nearest neighbour : sf(x,y) is gray value of its nearest pixel in A.
– Bilinear : sf(x,y) is weighted average gray value of its 4 neighbouring
pixels

Checkerboard effect

Blurring effect

•

Images are zoomed from 128x128, 64x64 and 32x32 sizes to 1024x1024. Top row
use the nearest neighbour interpolation, bottom row use Bilinear interpolation.

Image files Format

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Image files consists of two parts:


A header found at the start of the file and consisting of
parameters regarding:
 Number of rows (height)
 Number of columns (width)
 Number of bands (i.e. colors)
 Number of bits per pixel (bpp)
 File type



Image data which lists all pixel values (vectors) on the
first row, followed by 2nd row, and so on.

 Common image file formats include :
BIN, RAW, BMP, JPEG, TIFF, GIF, PPM, PBM, PGM, …

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Digital Image Processing system components

Digital Image Processing_ ch1 introduction-2003

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