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Digital image processing

Individual course

Please note that this course is arranged only in English.

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 07.05.2024 16-19 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

Responsible teacher

University of Oulu
Janne Heikkilä, Professor

Further information about the studies

University of Oulu
Course helpdesk

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
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AI and machine learning,
Data analytics,
Smart systems
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0 €
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Host university:
University of Oulu
Who can apply:
Adult learner,
Degree student
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General prerequisites:
Python programming skills, basic knowledge on signal processing, and basic engineering mathematics.
Course suitable for:
Students with background in technical mathematics or who have a BSc degree.
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