1. Digital Image Processing Course
Rahman Tashakkori, Appalachian
Sue Lea, UNCG
"A Consortium to Promote Computational Science and High Performance Computing"
Workshop – July 26-28, 2004

2. Introduction
This is an introductory course that covers fundamentals of digital image processing:
Image representation,
Enhancement,
Compression, and
Restoration.
The course is intended for upper level undergraduate students in computer science and other science majors.  
Prerequisites: Linear Algebra (Calculus II with the Instructor’s permission) and Data Structures. A course in statistics is very helpful.

3. Introduction Common topics discussed in an Image Processing Course:
Image acquistion and display,
Properties of the human visual system,
Color representations,
Sampling and quantization,
Point operations,
Linear image filtering and correlation,
Transforms and subband decompositions,
Nonlinear filtering, 
Contrast and color enhancement,
Dithering, and Image restoration,
Image registration,
Simple feature extraction, and
Recognition tasks.

4. Introduction

5. Digital Image Fundamentals
Lens is made of concentric layers of fibrous cells and is suspended by fibers that attach to the ciliary body. It contains 60% -70% water, 6% fat, and more protein than any other tissue in the eye.
There are 6 to 7 million cones in each eye. Cones are located at the central portion of the retina. They are highly color sensitive. We use them to resolve fine details. Cone vision is called photopic or bright-light vision.
There are million rods distributed over the retinal surface. Larger area of distribution and the fact that several of rods are connected to a single nerve, make them less effective for resolving details. Rod vision is called scotopic or dim-light vision.
Focal length 17 mm to about 14 mm

6. Grade Components Assignments
There will be about 6-7 assignments or labs with postlabs
Semester Project
The class projects will include a wide variety of topics and will be completed individually
Class Participation
Due to the fact that students may receive the course remotely, class participation is extremely important
Exams
There will be three exams including the final. These exams must be made such that the local coordinators on each campus are able to give them without any difficulties

7. Assignments
Contents
Assignments will reflect the topics covered in the course. These assignments will be available to all students via the web page of the course. Efforts should be made such that all submissions can be made electronically. For assignments which require submission of hardcopy the local and primary instructors should coordinate the submission process and the grading. Since each course have two instructors, one of the two instructors should receive the assignments and do the grading.
Grading
Assignments will have precise grading scale to reduce confusions and possible problems with grading, in particular, at the remote sites.
Due Date
All electronic submissions have a definite deadline as indicated on each assignment. Assignments which require hardcopies are due at the office of local course coordinator as indicated on the assignment.

8. Projects
Contents
Semester projects should be on one of the topics discussed in the course. These projects may include developing toolkits to perform image processing techniques, improving existing applications, or solving a theoretical problem.
Grading
Each project will be graded for originality, amount of work, and quality of the project itself and its documentation. All students are required to submit a report for their projects.
Due Date
All projects are due in electronic format. Students should submit electronic versions of their projects and all their program.
Presentations
Time permitting, selected number of projects will be presented by students. Also, selected toolkits will be used in our instruction in future semesters.

9. Class Participations
Importance
Due to the nature of this course and the way it is going to be offered both on and off campus, class attendance is extremely important. Perhaps, off campus students may be in disadvantage in such a case because they may not have access to the primary instructor to sit and discuss the details of the missing class. We should however prepare ourselves to deal with legitimate absences. That may require us to record the class sessions. I hope this does not encourage more absences. Major concern would be the breaks: fall and spring, etc …
Grading
In the past few semesters, I have been harsh in penalizing absences. This has really improved class attendance in all of my courses. These days, I have a very few, if any, absences for the entire semester. I have been penalizing 3 points on overall grade for each unexcused absence. It is not easy to justify an absence as an excused. I have given F to students with 8 or more absences.
Extreme Circumstance
Defined in my syllabus as being hospitalized or a death in the family.

10. Exams
Format
Perhaps this is the hardest part of our work. Giving exams that are suitable to all students in participating campuses is not an easy task. We are thinking of giving 3 exams, including the final. These exams are paper-based, thus, the local coordinators should mail them immediately after the exam. This way the primary instructor will have a chance to grade all the exams together.
Length
I have always faced difficulty giving 1-hour exams in this course. There is little or no chance for giving multiple choice exams or short answer exams. Usually, students solve a few problems during the exam period. This problem might become more noticeable at the remote sites.
Missing Exams and Extreme Circumstance
No make up exam policy with replacement of final grade for the missing exam under extreme circumstances.

11. Main Topics in the Textbook
1. Introduction.  2. Fundamentals.  3. Intensity Transformations and Spatial Filtering.  4. Frequency Domain Processing.  5. Image Restoration.  6. Color Image Processing.  7. Wavelets.  8. Image Compression.  9. Morphological Image Processing Image Segmentation Representation and Description.
12. Object Recognition.

12. 0. A Review of Linear Algebra and Statistics.
0.1 Matrix Manipulation (multiplication, addition, etc…)
0.1 Transformations
0.2 Inverse
0.3 etc.
Statistics
0.1 Mean
0.2 Standard division
0.3 Variance
0.4 PDF and CDF
0.5 etc.

13. 1. Introduction. Preview 1.1 Background
1.2 What Is Digital Image Processing?
1.3 Background on MATLAB and the Image Processing Toolbox
1.4 Areas of Image Processing Covered in the Book
1.5 The Book Web Site
1.6 Notation
1.7 The MATLAB Working Environment
1.7.1 The MATLAB Desktop
1.7.2 Using the MATLAB Editor to Create M-files
1.7.3 Getting Help
1.7.4 Saving and Retrieving a Work Session
1.8 How References Are Organized in the Book
Summary

14. 2. Fundamentals. Preview 2.1 Digital Image Representation
2.1.1 Coordinate Conventions
2.1.2 Images as Matrices
2.2 Reading Images
2.3 Displaying Images
2.4 Writing Images
2.5 Data Classes
2.6 Image Types
2.6.1 Intensity Images
2.6.2 Binary Images
2.6.3 A Note on Terminology
2.7 Converting between Data Classes and Image Types
2.7.1 Converting between Data Classes
2.7.2 Converting between Image Classes and Types
2.8 Array Indexing
2.8.1 Vector Indexing
2.8.2 Matrix Indexing
2.8.3 Selecting Array Dimensions
2.9 Some Important Standard Arrays
2.10 Introduction to M-Function Programming
M-Files
Operators
Flow Control
Code Optimization
Interactive I/O
A Brief Introduction to Cell Arrays and Structures 62
Summary

15. 3. Intensity Transformations and Spatial Filtering.
Preview
3.1 Background
3.2 Intensity Transformation Functions
3.2.1 Function imadjust
3.2.2 Logarithmic and Contrast-Stretching Transformations
3.2.3 Some Utility M-Functions for Intensity Transformations
3.3 Histogram Processing and Function Plotting
3.3.1 Generating and Plotting Image Histograms
3.3.2 Histogram Equalization
3.3.3 Histogram Matching (Specification)
3.4 Spatial Filtering
3.4.1 Linear Spatial Filtering
3.4.2 Nonlinear Spatial Filtering
3.5 Image Processing Toolbox Standard Spatial Filters
3.5.1 Linear Spatial Filters
3.5.2 Nonlinear Spatial Filters
Summary

16. 4. Frequency Domain Processing.
Preview
4.1 The 2-D Discrete Fourier Transform
4.2 Computing and Visualizing the 2-D DFT in MATLAB
4.3 Filtering in the Frequency Domain
4.3.1 Fundamental Concepts
4.3.2 Basic Steps in DFT Filtering
4.3.3 An M-function for Filtering in the Frequency Domain
4.4 Obtaining Frequency Domain Filters from Spatial Filters
4.5 Generating Filters Directly in the Frequency Domain
4.5.1 Creating Meshgrid Arrays for Use in Implementing Filters in the Frequency Domain
4.5.2 Lowpass Frequency Domain Filters
4.5.3 Wireframe and Surface Plotting
4.6 Sharpening Frequency Domain Filters
4.6.1 Basic Highpass Filtering
4.6.2 High-Frequency Emphasis Filtering
Summary

17. 5. Image Restoration. Preview
5.1 A Model of the Image Degradation/Restoration Process
5.2 Noise Models
5.2.1 Adding Noise with Function imnoise
5.2.2 Generating Spatial Random Noise with a Specified Distribution
5.2.3 Periodic Noise
5.2.4 Estimating Noise Parameters
5.3 Restoration in the Presence of Noise
Only—Spatial Filtering
5.3.1 Spatial Noise Filters
5.3.2 Adaptive Spatial Filters
5.4 Periodic Noise Reduction by Frequency Domain Filtering
5.5 Modeling the Degradation Function
5.6 Direct Inverse Filtering
5.7 Wiener Filtering
5.8 Constrained Least Squares (Regularized) Filtering
5.9 Iterative Nonlinear Restoration Using the Lucy-Richardson Algorithm
5.10 Blind Deconvolution
5.11 Geometric Transformations and Image Registration
Geometric Spatial Transformations
Applying Spatial Transformations to Images
Image Registration
Summary

18. 6. Color Image Processing.
Preview
6.1 Color Image Representation in MATLAB
6.1.1 RGB Images
6.1.2 Indexed Images
6.1.3 IPT Functions for Manipulating RGB and Indexed Images
6.2 Converting to Other Color Spaces
6.2.1 NTSC Color Space
6.2.2 The YCbCr Color Space
6.2.3 The HSV Color Space
6.2.4 The CMY and CMYK Color Spaces
6.2.5 The HSI Color Space
6.3 The Basics of Color Image Processing
6.4 Color Transformations

19. 8. Image Compression. Preview 8.1 Background 8.2 Coding Redundancy
8.2.1 Huffman Codes
8.2.2 Huffman Encoding
8.2.3 Huffman Decoding
8.3 Interpixel Redundancy
8.4 Psychovisual Redundancy
8.5 JPEG Compression
8.5.1 JPEG
8.5.2 JPEG 2000
Summary 333

20. 7. Wavelets. 9. Morphological Image Processing. 10. Image Segmentation
7. Wavelets. 9. Morphological Image Processing Image Segmentation Representation and Description.
12. Object Recognition.

21. Appalachian Calendar - AugustM T W Th F Sa 16 17 18 19 20 21 22 23 24
25ch1 26
27Rev 28 29
30Rev 31
3 classes at ASU … UNCG ?
Rev – Review (linear algebra and statistics)
UNCG has classes but no class at ASU

22. S M T W Th F Sa 1-ch2 2 3-ch2 4 5 6-lab1 7 8-ch3 9 10-ch3 11 12
Appalachian Calendar - September S M T W Th F Sa
1-ch2 2
3-ch2 4 5
6-lab1 7
8-ch3 9
10-ch3 11 12
13-lab2 14
15-ch3 16
17-ch4 18 19
20-lab3 21
22-ch4 23
24-ch4 25 26
27-ch4 28
29-ch4 30
13 classes

23. S M T W Th F Sa 1-ch5 2 3 4-lab4 5 6-ch5 7 8-ch5 9 10 11-lab5 12
Appalachian Calendar - October S M T W Th F Sa
1-ch5 2 3
4-lab4 5
6-ch5 7
8-ch5 9 10
11-lab5 12
*13-lab6
Lab 5 14 15
lab6 16 17
18-ch5 19
20-ch6 21
22-ch6 23 24
25-ch6 26
27-lab7 28
29-ch6 30
12 classes .. Oct 11, 12, and 13 Midterm week
Fall break ASU (Oct 11, and 12 UNCG)
* Rahman is out of town

24. S M T W Th F Sa 1ch7 2 3ch7 4 5lab8 6 7 8ch8 9 10ch8 11 12lab9 13 14
Appalachian Calendar - November S M T W Th F Sa
1ch7 2
3ch7 4
5lab8 6 7
8ch8 9
10ch8 11
12lab9 13 14
15ch8 16
17ch8 18
19sel 20 21
22sel 23 24 25 26 27 28
29prj 30
11 classes
Thanksgiving break

25. Appalachian Calendar - DecemberS M T W Th F Sa
1prj 2
3prj 4 5
6rev 7 *8 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
3 classes Total: 42 classes
Finals
* Reading day