1. Image and Video Processing: An Introduction and Overview
9/12/2018
Image and Video Processing: An Introduction and Overview
UMCP ENEE631 Slides (created by M.Wu © 2001)

2. Why Do We Process Images?
9/12/2018
Why Do We Process Images?
Enhancement and restoration
Remove artifacts and scratches from an old photo/movie
Improve contrast and correct blurred images
Composition (for magazines and movies), Display, Printing …
Transmission and storage
images from oversea via Internet, or from a remote planet
Information analysis and automated recognition
Providing “human vision” to machines
Medical imaging for diagnosis and exploration
Security, forensics and rights protection
Encryption, hashing, digital watermarking, digital fingerprinting …
UMCP ENEE631 Slides (created by M.Wu © 2001)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

3. UMCP ENEE631 Slides (created by M.Wu © 2001)
9/12/2018
Why Digital?
“Exactness”
Perfect reproduction without degradation
Perfect duplication of processing result
Convenient & powerful computer-aided processing
Can perform sophisticated processing through computer hardware or software
Even kindergartners can do some!
Easy storage and transmission
1 CD can store hundreds of family photos!
Paperless transmission of high quality photos through network within seconds
UMCP ENEE631 Slides (created by M.Wu © 2001)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

4. Examples of Digital Image & Video Processing
9/12/2018
Examples of Digital Image & Video Processing
Compression
Manipulation and Restoration
Restoration of blurred and damaged images
Noise removal and reduction
Morphing
Applications
Visual mosaicing and virtual views
Face detection
Visible and invisible watermarking
Error concealment and resilience in video transmission
UMCP ENEE631 Slides (created by M.Wu © 2001)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

5. UMCP ENEE631 Slides (created by M.Wu © 2001)
9/12/2018
Compression
Color image of 600x800 pixels
Without compression
600*800 * 24 bits/pixel = 11.52K bits = 1.44M bytes
After JPEG compression (popularly used on web)
only 89K bytes
compression ratio ~ 16:1
Movie ~ Image Sequence
720x480 per frame, 30 frames/sec, 24 bits/pixel
Raw video ~ 243M bits/sec
DVD ~ about 5M bits/sec
Compression ratio ~ 48:1
UMCP ENEE631 Slides (created by M.Wu © 2001)
“Library of Congress” by M.Wu (600x800)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

6. UMCP ENEE631 Slides (created by M.Wu © 2001)
9/12/2018
Denoising
UMCP ENEE631 Slides (created by M.Wu © 2001)
From X.Li
M. Wu: ENEE631 Digital Image Processing (Spring'09)

7. UMCP ENEE631 Slides (created by M.Wu © 2001)
9/12/2018
Deblurring
UMCP ENEE631 Slides (created by M.Wu © 2001)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

8. Special Effects: Morphing
9/12/2018
Special Effects: Morphing
UMCP ENEE631 Slides (created by M.Wu © 2001)
Use web browser to show this
Princeton CS426 face morphing examples
M. Wu: ENEE631 Digital Image Processing (Spring'09)

9. UMCP ENEE631 Slides (created by M.Wu © 2001)
9/12/2018
Face Detection
UMCP ENEE631 Slides (created by M.Wu © 2001)
Face detection in CMU CS,
Image enhancement, feature extractions, and statistical modeling are often important steps in computer vision tasks
See more image understanding examples by Prof. Chellappa’s research group (
M. Wu: ENEE631 Digital Image Processing (Spring'09)

10. 9/12/2018
Here are several pairs of pictures. We process the top ones to get the lower ones.
What were done here?
These pictures are taken from a thesis here. You can also get more information from the paper.
“General Illumination Correction and Its Application to Face Normalization”, J. Zhu et al, ICASSP 2003
M. Wu: ENEE631 Digital Image Processing (Spring'09)

11. Data Hiding for Annotating Binary Line Drawings
9/12/2018
Data Hiding for Annotating Binary Line Drawings
UMCP ENEE631 Slides (created by M.Wu © 2001)
marked w/ “01/01/2000”
pixel-wise difference
original
M. Wu: ENEE631 Digital Image Processing (Spring'09)

12. 9/12/2018
Error Concealment
25% blocks in a checkerboard pattern are corrupted
corrupted blocks are concealed via edge-directed interpolation
(a) original lenna image
(c) concealed lenna image
(b) corrupted lenna image
UMCP ENEE631 Slides (created by M.Wu © 2001)
Examples were generated using the source codes provided by W.Zeng.
M. Wu: ENEE631 Digital Image Processing (Spring'09)

13. So What’s a Digital Image After All?
9/12/2018
So What’s a Digital Image After All?
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

14. UMCP ENEE631 Slides (created by M.Wu © 2007)
9/12/2018
What is an Image?
What we perceive as a grayscale image is a pattern of light intensity over a 2-D plane (aka “image plane”)
Described by a nonnegative real-valued function I(x,y) of two continuous spatial coordinates on an image plane.
I(x,y) is the intensity of the image at the point (x,y).
An image is usually defined on a bounded rectangle for processing
I: [0, a]  [0, b]  [0, inf )
Color image
Can be represented by three functions:
R(x,y) for red, G(x,y) for green, B(x,y) for blue.
UMCP ENEE631 Slides (created by M.Wu © 2007)
Now, what is an image?
We call this image an analog image, because …
For the time being, we consider a digital image to be a set of numbers.
gray scale image. x y
M. Wu: ENEE631 Digital Image Processing (Spring'09)

15. Different Ways to View an Image
9/12/2018
Different Ways to View an Image
(More generally, to view a 2-D real-valued function)
Intensity visualization over 2-D (x,y) plane
In 3-D (x,y, z) plot with z=I(x,y); red color for high value and blue for low
Example from B. Liu – EE488 F’06 Princeton
Equal value contour in (x,y) plane
M. Wu: ENEE631 Digital Image Processing (Spring'09)

16. Sampling and Quantization
9/12/2018
Sampling and Quantization
Computer handles “discrete” data.
Sampling
Sample the value of the image at the nodes of a regular grid on the image plane.
A pixel (picture element) at (i, j) is the image intensity value at grid point indexed by the integer coordinate (i, j).
Quantization
Is a process of transforming a real valued sampled image to one taking only a finite number of distinct values.
Each sampled value in a 256-level grayscale image is represented by 8 bits.
=> Stay tuned for the theories on these in future weeks.
UMCP ENEE631 Slides (created by M.Wu © 2001)
0 (black)
255 (white)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

17. Recall: 1-D Sampling Theorem
9/12/2018
Recall: 1-D Sampling Theorem
1-D Sampling Theorem
A 1-D signal x(t) bandlimited within [-B,B] can be uniquely determined by its samples x(nT) if s > 2B (i.e. sample fast enough).
Using the samples x(nT), we can reconstruct x(t) by filtering the impulse version of x(nT) by an ideal low pass filter
Sampling below Nyquist rate (2B) cause Aliasing
=> Will extend sampling theorem to 2-D later in the course
UMCP ENEE631 Slides (created by M.Wu © 2002)
Xs() with s > 2B  Perfect Reconstructable
s=2/T B
-s
(hidden)
Xs() with s < 2B  Aliasing
s=2/T B
M. Wu: ENEE631 Digital Image Processing (Spring'09)

18. UMCP ENEE631 Slides (created by M.Wu © 2001)
9/12/2018
Examples of Sampling
256x256
64x64
16x16
UMCP ENEE631 Slides (created by M.Wu © 2001)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

19. Examples of Quantizaion 
9/12/2018
Examples of Quantizaion 
8 bits / pixel
4 bits / pixel
2 bits / pixel
UMCP ENEE631 Slides (created by M.Wu © 2001)
M. Wu: ENEE631 Digital Image Processing (Spring'09)

20. An Ancient Example of Digital Image 
9/12/2018
An Ancient Example of Digital Image 
An Old “Digital” Picture (from a small church in Crete Island, Greece)
=> Colored tiles as “pixels”
Digital Image is not new. There have been digital images since the ancient times.
This is a picture in a small church in Crete. It is a digital picture, made of colored tiles
Slide from B. Liu – EE488 F’06 Princeton
M. Wu: ENEE631 Digital Image Processing (Spring'09)

21. UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
9/12/2018
Color of Light
Perceived color depends on spectral content (wavelength composition)
e.g., 700nm ~ red.
“spectral color”
A light with very narrow bandwidth
A light with equal energy in all visible bands appears white
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
“Spectrum” from
M. Wu: ENEE631 Digital Image Processing (Spring'09)

22. Perceptual Attributes of Color
9/12/2018
Perceptual Attributes of Color
Value of Brightness (perceived luminance)
Chrominance
Hue
specify color tone (redness, greenness, etc.)
depend on peak wavelength
Saturation
describe how pure the color is
depend on the spread (bandwidth) of light spectrum
reflect how much white light is added
RGB  HSV Conversion ~ nonlinear
UMCP ENEE408G Slides (created by M.Wu & R.Liu © 2002)
HSV circular cone is from online documentation of Matlab image processing toolbox
M. Wu: ENEE631 Digital Image Processing (Spring'09)

23. Questions for Today (QFT)
9/12/2018
“Seeing yellow” figure is from B.Liu ELE330 S’01 lecture Princeton; primary color figure is from Chapter 6 slides at Gonzalez/ Woods DIP book website
Questions for Today (QFT)
Why “seeing yellow without yellow”?
mix green and red light to obtain the perception of yellow, without shining a single yellow light
520nm
630nm
570nm =
M. Wu: ENEE631 Digital Image Processing (Spring'09)