The document provides an overview of digital image processing. It discusses why DIP is needed, defines what a digital image is, and outlines the basic steps of digitizing an image through sampling and quantization. It also describes several applications of DIP in industries like traffic control and management, entertainment, and medicine. The document aims to introduce readers to the fundamental concepts and objectives of the field of digital image processing.

1. DIGITAL IMAGE PROCESSING
ANKU SAINI
10905163
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2. Why do we need to study DIP?
Interest in digital image processing methods
stems from two aspects:
1) Improve images for human interpretation;
2) Process images, include storage,
transmission.
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3. 1.1 What is a digital image ?
. An image may be defined a two-dimension function f(x,y)
(x,y) ---- coordinate for a point in a plane.
f ---- intensity or gray or color in the position (x,y)
. Digital image
digitize to the function f and coordinates (x,y) let them
become discrete values.
Usually f,x and y are all finite values.
. Pixel
a point in a digital image is called pixel.
. Digital Image Processing
regarded as a discipline from an image to another.
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4. 1.2 The Origins of Digital image Processing
. The first application was in newspaper industry in 1920s
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5. Digital Image Processing, 2nd ed.
Digital Processing, 2nd ed. www.imageprocessingbook.com
Chapter 1: Introduction
They were not real image processing, only image encoding and printing
and some improvements.
© 2002 R. C. Gonzalez & R. E. Woods
In 1960s, computer and its programming were brought into, the true
image processing began.
Two important events push forward digital image processing.
1) Space Program --- First Moon Probe ( America, in 1964);
2) In medicine --- CAT or CT (Computer Axial Tomography early 1970s)
Using X-rays generates image.
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6. 1.3 Objectives of digital image processing
1.Improve qualities of images so that human can interpret
them better.
Such as enhancement, restoration and so on.
2.Process pictures and extract some information from them
for machine perception.
Such as image analysis, image recognition and so on.
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7. 1.4 Three level-processes for a digital image
.Low-level processes: reduce noises, contrast enhancement and
so on, from an image to another, improve the image quality;
.Mid-level processes: extract some attributes from an image,
segment an image, extract object contour in an image
.High-level processes: recognize objects in an image for analysis
There is no obvious boundary between digital image processing
and computer vision.
Computer vision: Machine perception based on vision or to use
computers to emulate human vision. Image recognition is a little like
this.
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8. 1.5 What can digital image processing do?
. Digitizing an image ( convert an continuous image to
a digital one)
. Enhancing an image ( Let an image better suit for a specific
application)
. Restoring an image ( Recover a damaged image)
. Compressing an image ( Store it with less bytes )
. Segmenting an image ( Partition objects in an image from
background )
. Recognizing an image ( Tell what the objects are in an image )
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9. 1.5.1 Digitizing an image
It is the first step of digital image processing
Y
. Sample ( like these grids)
. quantization
F(x,y)
X
x
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10. 1.5.2 Enhancing an image
.
Original Image
Enhanced Image
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11. 1.5.3 Restoring an image
When an image damaged, we can recover it torn
Cracked parts
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12. 1.5.4 Compressing an image
Original image 257kb Compressed image 147kb
Redundant Info.
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13. Segmenting an
image
Original image Segmented image
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14. 1.5.6 Recognizing an image
Take car license plate recognition as an example.
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15. 1.6 Digital image processing and computer graphics.
. The differences between them can be shown as follows.
Image1 Image2
image processing
Data Image3
Computer Graphics
Image1 and Image2 are different: Image2 is gotten by processing image1;
Image3 is produced or generated by converting data, which maybe a
virtual image; Some examples are as follows.
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16. Some examples about their differences.
A simple example for computer graphics is that when we input the
center coordinates (x,y) and a radius R, a circle ( image) can be
produced by computer graphics system.
R
(x,y)
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17. 1.7 The flow of a typical digital image processing system
Original Processed image
image
Camera or Pre- An interpretation
Processing
Scanner processing
Control
Signals
Digitization Enhancement Restoration Controlled Devices
Compression
Recognition
Analysis
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18. 1.8 The elements of a digital image processing system
Image acquisition: Digital camera or scanner or video camera;
Image storage: all kinds of digital memory, such as hard disk,
tape, optical disk and so on;
Image processing: Computers with software;
Image display: Displayer or all kinds of hardcopy devices.
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19. 1.9 Some Applications of digital image processing
It is widely used in industry,medical image, commerce,
entertainment and so on.
(1) Industry monitoring system
e.g. Temperature control ,
automatically adjust
temperature based on
the color in the flame image.
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20. (3) Traffic Management
The key is car license plate recognition based on image
--- Acquiring the image of a car license plate
Using camera or video camera
--- Enhancement processing
Adjusting the distribution of the gray level in an image
--- Segmentation
Segmenting letters or digitals in the plate
--- Recognition
Telling what the letters or digitals are
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21. (4) Traffic Control
. It can be widely used to the following aspects
.. Charge automatically on freeway
--- Auto-record the car license plate
--- Distinguish the type of car
--- Recognize the plate
--- Connecting the credit card system automatically
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22. (5) Traffic Control
.. Park management
Automatically record the car license plate and recognize it
and control passing-bar to switch on or off.
.. Monitor the driver with over-speed on freeway
automatically record the car license plate with over-speed
and recognize it.
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23. (6) Entertainment
An example --- Human face beautifying
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24. 1.10 Some examples of Using DIP Based on EM spectrum
(1) The electromagnetic spectrum arranged according to energy per photon.
(2) X-ray and Visual bands of spectrum are the most familiar images in
actual application, such as X-ray in medical inspection and so on.
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25. (3) Gamma-Ray Imaging
Nuclear medicine: Inject a patient with a radioactive isotope that can emit
gamma-ray. It is used in locating sites of bone pathology,such as infection
or tumors.
PET --- Positron Emission Tomograph
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27. (4) X-ray Imaging
Be widely used in Medical diagnostics, Industry, Astronomy and so on.
When X-rays penetrate an objects, there is a different amount of absorption
for different parts in the object , so an image is generated in the film to be
sensitive to X-ray energy.
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29. (5) Imaging in the Ultraviolet Band
Applications of ultraviolet “light” include lithography,industrial
inspection, microscopy,lasers,biological imaging,and astronomical
observations.
i) Ultraviolet light is used in fluorescence
microscopy.
ii) Fluorescence microscopy is an
excellent method for studying
material that can be made to
fluorescene.
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30. (6) Imaging in the Visible and Infrared Bands
T
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31. These examples range from
pharmaceuticals and micro-
inspection to materials
characterization.
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32. iii) Weather observation and prediction also are major
applications of multi-spectral imaging from satellites.
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33. (7) Imaging in the Microwave Band
A typical application in the microwave band is radar.
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34. (8) Imaging in the Radio Band
The major applications in the radio band are in medicine and
astronomy. In medicine radio waves are used in magnetic
resonance imaging (MRI).
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35. Comparison with other bands
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36. 1.11 How to digitize an image
For an image we must digitize it so that it can be
processed by computers.
For an image, we usually use the intensity function
f(x,y) to represent it.
(x,y) --- the location of a point in the image;
f (x,y) --- the intensity of the point (x,y);
It is obvious that 0< f(x,y) < ∞
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37. A simple model is f(x,y)= i(x,y)r(x,y)
i(x,y) --- intensity of the incident light
0 < i (x,y) < ∞
r(x,y) --- the coefficient of the reflection,
depend on the object light casts
0 < r(x,y) < 1
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38. Take a picture as an example.
Y
f (x,y)
X
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39. Sampling --- Digitize the spatial coordinates ( pixel )
Quantizing --- Digitize the intensity function f (x,y)
An image processed by sampling and quantizing is called
the digital image.
It is also the procedure from a continuous image to
a discrete one.
Uniform sampling --- If all sampled points are equal spaces
Uniform quantizing --- If all gray-level intervals are the same
( From the darkest to the brightest )
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40. Suppose there are N pixels along horizontal direction X and
M pixels along vertical direction Y.
and there are L gray levels, a digital image can be represented
by the following matrix.
 f (0,0) f (0,1) .... .... f (0, M − 1) 
 f (1,0) f (1,1) .... .... f (1, M − 1) 
 
f ( x, y ) ≈  .... .... .... .... .... 
 
 .... .... .... .... .... 
 f ( N − 1,0)
 f ( N − 1,1) f ( N − 1, M − 1)

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41. For any point (x,y) in the digital image
x ∈ [ 0,1,......N − 1] y ∈ [ 0,1,......M − 1]
f ( x, y ) ∈ [ 0,1,......L − 1]
n m k
Usually, N =2 and M = 2 L= 2
So the number, b, of bits required to store a digital image:
b = M ×N×k
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42. As for the quality of a digital image, the larger are M, N
and L, the better is the image. 2
For a square image, we have M=N, so b = N ×m
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