Digital image processing

Max amount of FITech students: 30

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

The course gives an introduction to digital image processing methods. The emphasis is in various image filtering techniques implemented both in spatial and frequency domains. Also, image compression and basic image segmentation methods are covered. Homework assignments using Jupyter notebooks and Python programming language provide practical experience for applying the methods to real photographs.

Course contents

1. Introduction
2. Fundamentals of digital image
3. Intensity transformations and spatial filtering
4. Image processing in frequency domain
5. Restoration
6. Color image processing
7. Wavelets and multi-scale processing
8. Compression
9. Morphological image processing
10. Segmentation

Learning outcomes

Upon completion of the course you

• understand the basic theory of digital image processing and know its main applications
• are able to apply spatial and frequency domain and wavelet based methods in image enhancement, restoration, compression and segmentation.

Course material

Lecture and exercise material, online lecture videos, Jupyter notebook guides and tutorials. Technology: Anaconda Python Individual Edition (free for non-commercial use).

Teaching schedule

• Lectures on Mon 10-12 and Wed 8-10 (recordings in Moodle)
• Exercises Thu 14-16
• Exam in Moodle.

More information in the University of Oulu study guide.

You can get a digital badge after completing this course.

image processing photo editing spatial domains frequency domains image restoration wavelets compression segmentation konenäkö machine vision