Digital Image Processing
Lecture 1
Introduction
Fall 2019

Introduction to the course
► Class Time
8:10 – 8:50 & 8:55 – 9:35 Monday 1A102
Week 1-16
13:30 – 15:30 Wednesday
Week 3-
►Instructor
Wen Shi (shiwen@wzu.edu.cn)

Introduction to the course
► Grading
 Attendance: 10%
 Class participation: 10%
 Lab practice : 30%
 Final report: 50%
 Total: 100%

► Applications
Face modeling
Introduction to the course

► Applications
Face generator Object generator
Introduction to the course

► Applications
Style transfer
Introduction to the course

► Applications
Expression recognition Object recognition
Introduction to the course

► Applications
Super-resolution Compression
Introduction to the course

Introduction to the course
► Textbooks
Digital Image Processing, by Rafael Gonzalez and Richard Woods

Introduction to the course
► Useful references but are not required
Computer Vision: Algorithms and Applications, by Richard Szeliski
Multiple View Geometry in Computer Vision, by Richard Hartley
Computer Vision: A Modern Approach, by David Forsyth and Jean Ponce.
Photography, by Barbara London and John Upton

►Lectures
 Signals and systems. Discrete sequences and systems, their types
and properties. Linear time-invariant systems, convolution.
 Phasors. Eigen functions of linear time-invariant systems. Review of
complex arithmetic. Some examples from electronics, optics and acoustics.
 Fourier transform. Phasors as orthogonal base functions. Forms of
the Fourier transform. Convolution theorem, Dirac’s delta function, impulse
combs in the time and frequency domain.
 Discrete sequences and spectra. Periodic sampling of continuous
signals, periodic signals, aliasing, sampling and reconstruction of low-pass
and band-pass signals, spectral inversion.
 Discrete Fourier transform. Continuous versus discrete Fourier
transform, symmetry, linearity, review of the FFT, real-valued FFT.

►Lectures
 Correlation coding. Random vectors, dependence versus correlation,
covariance, decorrelation, matrix diagonalization, eigen decomposition,
Karhunen-Loève transform, principal component analysis. Relation to
orthogonal transform coding using fixed basis vectors, such as DCT.
 Lossy versus lossless compression. What information is
discarded by human senses and can be eliminated by encoders? Perceptual
scales, masking, spatial resolution, colour coordinates, some demonstration
experiments.
 Quantization, image/video coding standards. A/mu-law
coding, delta coding, JPEG, H.264, HEVC.

Introduction to the course
► Article Reading
 Medical image analysis (MRI/PET/CT/X-ray tumor
detection/classification)
 Face, fingerprint, and other object recognition
 Image and/or video compression
 Image segmentation and/or denoising
 Digital image/video watermarking/steganography and
detection
 Whatever you’re interested …

Journals & Conferences
in Image Processing
► Journals:
— IEEE T IMAGE PROCESSING
— IEEE T MEDICAL IMAGING
— INTL J COMP. VISION
— IEEE T PATTERN ANALYSIS MACHINE INTELLIGENCE
— PATTERN RECOGNITION
— COMP. VISION AND IMAGE UNDERSTANDING
— IMAGE AND VISION COMPUTING
… …
► Conferences:
— CVPR: Comp. Vision and Pattern Recognition
— ICCV: Intl Conf on Computer Vision
— ACM Multimedia
— ICIP
— SPIE
— ECCV: European Conf on Computer Vision
— CAIP: Intl Conf on Comp. Analysis of Images and Patterns
… …

Introduction
► What is Digital Image Processing?
Digital Image
— a two-dimensional function
x and y are spatial coordinates
The amplitude of f is called intensity or gray level at the point (x, y)
Digital Image Processing
— process digital images by means of computer, it covers low-, mid-, and high-level
processes
low-level: inputs and outputs are images
mid-level: outputs are attributes extracted from input images
high-level: an ensemble of recognition of individual objects
Pixel
— the elements of a digital image
( , )
f x y

Introduction
123 33 234 45 67 90 12 134
34 56 89 54 67 98 111 56 67
90 65 34 ….
The World
Numerical representation of the
brightness and colors of the world
scene

Introduction
► Mainly study these topics
Image acquisition – (low-level) digital representation of the world
scenes
Image processing – noise removal, smoothing, sharpening, contrast
enhancement, alter the appearance of an image
Image compression – efficiently represent image data for storage (save
disk space) and communication (save network bandwidth) .
Display – render the image data on reproduction media (monitors,
printing papers)

Introduction
► More related subjects
Artificial intelligence
Pattern recognition
Machine learning
Robotics
Visualization

Image Processing
► Image acquisition – (low-level) digital representation of the
world scenes
123 33 234 45
67 90 12 134 34
56 89 54 67 98
111 56 67 90 65
34 ….
Numbers represent the brightness and colors
of the world objects, but we have no
knowledge what object, e.g., books, monitors,
these numbers contain – hence low-level

Image Processing
► Image acquisition – (low-level) digital representation of the
world scenes
123 33 234 45
67 90 12 134 34
56 89 54 67 98
111 56 67 90 65
34 ….
What numbers?
How many numbers?
How large/small should the numbers be?

Image Processing
► Image processing – noise removal, smoothing, sharpening,
contrast enhancement, alter the appearance of an image
Noise removal

Image Processing
► Image processing – noise removal, smoothing, sharpening,
contrast enhancement, alter the appearance of an image
Sharpening

Image Processing
► Image processing – noise removal, smoothing, sharpening,
contrast enhancement, alter the appearance of an image
Blurring/smoothing

Image Processing
► Image processing – noise removal, smoothing, sharpening,
contrast enhancement, alter the appearance of an image
Contrast
enhancement

Image Processing
► Image processing – noise removal, smoothing, sharpening,
contrast enhancement, alter the appearance of an image
Alter
appearance

Image Processing
► Image compression – efficiently represent image data for
storage (save disk space) and communication (save network
bandwidth)
245,760 bytes 69,632 bytes 5,951 bytes

Image Processing
► Display – render the image data on reproduction media
(monitors, printing papers)
123 33 234 45
67 90 12 134 34
56 89 54 67 98
111 56 67 90 65
34 ….

Image Processing
► Display – render the image data on reproduction media
(monitors, printing papers)
123 33 234 45
67 90 12 134 34
56 89 54 67 98
111 56 67 90 65
34 ….

Sources for Images
► Electromagnetic (EM) energy spectrum
► Acoustic
► Ultrasonic
► Electronic
► Synthetic images produced by computer

Electromagnetic (EM) energy spectrum
Major uses
Gamma-ray imaging: nuclear medicine and astronomical observations
X-rays: medical diagnostics, industry, and astronomy, etc.
Ultraviolet: lithography, industrial inspection, microscopy, lasers, biological imaging,
and astronomical observations
Visible and infrared bands: light microscopy, astronomy, remote sensing, industry,
and law enforcement
Microwave band: radar
Radio band: medicine (such as MRI) and astronomy

Examples: Gama-Ray Imaging

Examples: X-Ray Imaging

Examples: Ultraviolet Imaging

Examples: Light Microscopy Imaging

Examples: Visual and Infrared Imaging

Examples: Visual and Infrared Imaging

Examples: Infrared Satellite Imaging

Examples: Automated Visual Inspection

Examples: Automated Visual Inspection
The area in which
the imaging
system detected
the plate
Results of
automated
reading of the
plate content by
the system

Example of Radar Image

Examples: MRI (Radio Band)

Examples: Ultrasound Imaging

Fundamental Steps in DIP
Result is more
suitable than
the original
Improving the
appearance
Extracting image
components
Partition an image into
its constituent parts or
objects
Represent image for
computer processing