Course Catalog 2010-2011
International

Basic Pori International Postgraduate Open University

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Course Catalog 2010-2011

SGN-3016 Digital Image Processing I, 5 cr

Person responsible

Serkan Kiranyaz, Moncef Gabbouj

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises
 2 h/week
 2 h/week
+2 h/week
+2 h/week


 


 


 
SGN-3016 2010-01 Friday 12 - 14, TB223

Requirements

Exercises and final exam plus optional computer project.
Completion parts must belong to the same implementation

Principles and baselines related to teaching and learning

- Power point presentations with animations will be the primary tools used in the lectures - white board will also be used to illustrate the concepts - hands on are given during the exercise sessions - each student will carry out the exercises using mostly Matlab programming tools

Learning outcomes

This course is designed to help the student: • Apply principles and techniques of digital image processing in applications related to digital imaging system design and analysis. • Analyze and implement image processing algorithms. • Gain hands-on experience in using software tools for processing digital images.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Students learn the building blocks of a digital image processing system, and learn about the different types of images to be processed in the course as well as the type of problems to be solved.  Sampling theorem and theory of quantization.  Nonlinear filtering. 
2. Students then learn the principles of image formation, sampling, quantization and the human visual system, which will allow them to investigate specific image processing techniques later on.  Fourier and other Transforms.  Image analysis and segmentation. 
3. The first major task to be learned is image intensity transformations and spatial filtering for the purpose of image enhancement in the spatial and frequency domains.  Impulse response and frequency response of Linear Filters.   
4. The second major task is image restoration in the spatial and frequency domains. Students learn how to deal with different types of noise models and degradation processes. Then they learn about inverse filtering and Wiener filtering.  Theory of transforms.   
5. Finally, the students will learn about color spaces and color image processing and how to restore and enhance color images and different color spaces.  Colorimetry and color science.   

Evaluation criteria for the course

Course Outcomes: This course requires the student to demonstrate the ability to: 1. Explain the basic elements and applications of image processing 2. Analyze image sampling and quantization requirements and implications 3. Perform Gray level transformations for Image enhancement 4. Apply histogram equalization for image enhancement 5. Use and implement order-statistics image enhancement methods 6. Design and implement two-dimensional spatial filters for image enhancement 7. Model the image restoration problem in both time and frequency domains 8. Explain Wiener filtering for de-blurring and noise removal 9. Explain the representation of colors in digital color images 10. Use Matlab to implement different image processing tasks 11. Document implementation code, report experimental results and draw proper conclusions 12. Prepare and submit a (optional) project report. Course assessment criteria: Grading is on a scale of 1-5. In order to pass the course, students must collect at least half the points from the final exam and attend at least 8 exercise sessions. A project work may also be assigned.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   "Digital Image Processing"   R. Gonzalez and R. Woods       http://www.imageprocessingplace.com/, 3rd edition, Prentice-Hall, New Jersey, 2008      English  

Prerequisites

Course Mandatory/Advisable Description
OHJ-1100 Programming I Advisable    
OHJ-1106 Programming I Advisable    
SGN-1107 Introductory Signal Processing Mandatory    
SGN-1157 Signal Processing Basics Mandatory    

Additional information about prerequisites
Equivalent knowledge of the basics of digital signal processing is required.

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-3016 Digital Image Processing I, 5 cr 8002103 Digital Image Processing, 3 cu  

Additional information

This course is equivalent to SGN-3010

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-3016 2010-01 Digital Image Processing        

Last modified03.02.2011