This document provides an introduction to a digital image processing course. It outlines the class times, instructor, textbooks, grading structure, and topics to be covered including image acquisition, processing, compression, and display. Image processing involves tasks like noise removal, sharpening, blurring, and contrast enhancement to alter image appearance. Image compression aims to reduce file sizes for storage and transmission. The course will also explore image sources in the electromagnetic spectrum and medical imaging modalities.

1. Digital Image Processing
Lecture 1
Introduction
Fall 2019

2. Introduction to the course
► Class Time
9:55 – 10:35 & 10:40 – 11:20 Monday 1A102
Week 1-16
15:45 – 17:45 Wednesday
Week 3-
►Instructor
Wen Shi (shiwen@wzu.edu.cn)

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

4. Introduction to the course
► Grading
 Class participation: 30%
 Assignments: 30%
 Exam: 40%
 Total: 100%

5. 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 …

6. 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
… …

7. 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

8. 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

9. 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)

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

11. 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

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

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

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

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

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

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

18. 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

19. 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 ….

20. 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 ….

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

22. 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

23. Examples: Gama-Ray Imaging

24. Examples: X-Ray Imaging

25. Examples: Ultraviolet Imaging

26. Examples: Light Microscopy Imaging

27. Examples: Visual and Infrared Imaging

28. Examples: Visual and Infrared Imaging

29. Examples: Infrared Satellite Imaging

30. Examples: Automated Visual Inspection

31. 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

32. Example of Radar Image

33. Examples: MRI (Radio Band)

34. Examples: Ultrasound Imaging

35. 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