Course Catalog 2010-2011
SGN-3016 Digital Image Processing I, 5 cr
Serkan Kiranyaz, Moncef Gabbouj
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
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||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.
Numerical evaluation scale (1-5) will be used on the course
|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|
|OHJ-1100 Ohjelmointi 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
This course is equivalent to SGN-3010