3. 3
Course Info
Textbook
R. C. Gonzalez and R. E. Woods, DIGITAL IMAGE
PROCESSING, 2nd edition, Prentice Hall, 2002
Reference
R. C. Gonzalez and R. E. Woods, DIGITAL IMAGE
PROCESSING with MATLAB , Prentice Hall,
Anil K. Jain, FUNDAMENTALS OF DIGITAL IMAGE
PROCESSING, Prentice Hall
5. 5
DIP?
Image processing is a subclass of signal
processing concerned specifically with
pictures.
Improve image quality for human perception
and/or computer interpretation
6. 6
DIP?
The Field of DIP refers to processing of digital
images by means of Digital computers
Two principal application
Improvement of pictorial info. For human interpretation
Processing of image data for
Storage
Transmission
Representation for autonomous machine perception
7. 7
Historical Developments of DIP
1920’s: Analog image transmission
Transatlantic picture transmission
London – New York
1960’s: Space program
Invention of digital computer
hardware and software
Source data is very expensive
1970’s: X-ray imaging (CT)
Nowadays: Cheap computing
Lots of DIP applications
8. 8
Scope
Computer vision aims to duplicate the effect of
human vision by electronically perceiving and
understanding an image.
Not an easy task
Three dimensional (3D) world,
available visual sensors (e.g., TV cameras) usually give
two dimensional (2D) images
this projection to a lower number of dimensions incurs an
enormous loss of information.
9. 10
Several fields deal with images
Computer Graphics : the creation of images.
Image Processing : the enhancement or
other manipulation of the image
the result of which is usually another images.
Computer Vision: the analysis of image
contents
10. 11
Typical DIP system
Digital Image Processing is processing of two-
dimensional data by a digital computer
11. 12
Digital Image
Image
Two-dimensional function f(x,y)
x, y : spatial coordinates
Value of f : intensity or gray level
A set of pixels
Pixel means
pixel coordinates/location
pixel value
or both
Both coordinates and value are discrete
13. 14
Examples of Fields that use DIP
Categorize by image sources
Radiation from the Electromagnetic spectrum
Acoustic
Ultrasonic
Subsonic
Electronic (in the form of electron beams used in
electron microscopy)
Computer (synthetic images used for modeling and
visualization
14. 15
EM spectrum
EM waves = a stream of mass-less (proton)
particles, each traveling in a wavelike pattern and
moving at the speed of light.
Spectral bands are grouped by energy per photon
Gamma rays, X-rays, Ultraviolet, Visible, Infrared,
Microwaves, Radio waves
15. 16
Gamma-Ray Imaging
Nuclear medicine
Bone Scan
PET Scan (Positron emission
tomography) image
Astronomical observation
Cygnus Loop
Nuclear Reaction
shows an image of Gamma
radiation from a reactor valve
18. 19
Visible& infrared Imaging
Medical microscopy
Anticancer medicine(250x)
Cholesterol (40x)
Industrial applications
Microprocessor (60x)
Nickel oxide film (600x)
Surface of audio CD (1750x)
Organic superconductor(450x)
19. 20
Imaging in Microwave Band
Imaging radar : the only way
to explore inaccessible
regions of the Earth’s surface
Radar image of mountains in
southeast Tibet
Note the clarity and detail of the
image, unencumbered by
clouds or other atmospheric
conditions that normally
interfere with images in the
visual band
20. 21
Imaging in Radio Band
Medicine
Magnetic resonance
image (MRI) : 2D
picture of a section of
the patient (any plane)
knee
spine
Astronomy
21. 22
Acoustic Imaging
Geological applications use
sound in the low end of the
sound spectrum (hundred of
Hz)
Mineral and oil exploration
Cross-sectional image of a
seismic model
22. 23
Ultrasound images
Transmit ultrasound (1 to
5MHz)
Sound pulses are
reflected from tissues
Compute the distance
using the speed
Display the distances and
intensities of the echoes
23. 24
3 types of computerized
process
Low-level : input, output are images
Primitive operations such as image preprocessing to reduce
noise, contrast enhancement, and image sharpening
Mid-level : inputs may be images, outputs are attributes
extracted from those images
Segmentation
Description of objects
Classification of individual objects
High-level :
Image analysis
Image Understanding
24. 25
Computational Complexity of DIP
HDTV quality video
Resolution : 1024 x 768
786,432 pixels
Refresh rate : 30 pictures/s
Compute average gray level of each picture
786,432 x 30 = about 23 million additions/s
Digital video processing had not been possible for a
long time
29. 30
Image Enhancement
To bring out detail, which is
obscured, or simply to
highlight certain features of
interest in an image.
To accentuate certain image
features for subsequent
analysis or for image display
Subjective process
g(x,y) = 255 – f(x,y)
34. 35
Image Compression
Objective
To reduce the amount
of data to represent
images
From the bit stream,
the approximate copy
of
the original image can
be reproduced
35. 36
Image Compression
JPEG compression
1000 x 1000 RGB picture = 3 MB
16 MB memory card can store only 5 pictures
With JPEG, the same card can store more than 80
pictures
36. 37
Image Compression
Video compression
HDTV quality video
1024 x 768 x 3 x 30 x 8 = 566 Mbits/s (Mbps)
Video compression standards
MPEG-1: Video-CD, 1-2 Mbps
MPEG-2: HDTV and DVD, 2-15 Mbps
H.263: Low bit-rate applications,
10-2048 Kbps
MPEG-4: similar to H.263
H.264/AVC : new video coding standard
43. 44
Sampling and Quantization
Computers cannot process
parameters/functions that vary in a continuum
We have to discretize:
x, y => xi , yj where (i = 0; : : : ;N 1; j = 0; : : : ;M ¡
1): Sampling
f(x, y) =>F(xi , yj)
Quantization
47. 48
Objectives ( C_1)
To define the scope of DIP
To give historical perspective of the origins of this
field
To give an Idea of the stat of the art in DIP by
examining some of the principal areas in which this
field is applied
To give an overview of the components contained in
a typical DIP system
