Postgraduate course

Image Processing

  • ECTS credits10
  • Teaching semesterSpring
  • Course codeMAT262
  • Number of semesters1
  • Language


  • Resources

Teaching semester


Objectives and Content

The course deals with basic algorithms and mathematical theory that constitute foundation for classical and modern digital image analysis. The classical part of the course deals with understanding digital images, basic manipulations based on the image histogram smoothing and sharpening by spatial filters, elementary image registration. Further, Fourier analysis, Fast Fourier Transformations, wavelet analysis and also digital filter theory will be considered. We also consider edge detection and thresholding. The modern part gives an overview with segmentation using watersheds, noise removal by Rdin-Osher-Fatemi model, graph cuts, optimization models for image registration, active contours and level set methods.

Learning Outcomes

To provide a solid knowledge and understanding of the most important algorithms: the mathematical theory behind them, their numerical stability and efficiency. The course is very useful for master students in computational mathematics.

Required Previous Knowledge


Recommended Previous Knowledge

MAT160 Scientific Computing I

Compulsory Assignments and Attendance


Forms of Assessment

Written exam. Exercises might be graded and included in the final grade. It may be oral examination if less than 20 students attend the course.

Grading Scale

The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.


Contact Information


Exam information

  • Type of assessment: Oral examination

    Withdrawal deadline