NDAA09027U Signal and Image Processing (SIP)

Volume 2024/2025

MSc Programme in Computer Science
MSc Programme in Physics


The course introduces basic computational, statistical, and mathematical techniques for representing, modeling, and analysing signals and images. Signals and images are measurements, which are correlated over time and/or space, and these measurements typically originate from a physical system ordered on a grid. Examples are 1-dimensional sound, 2-dimensional images from a consumer camera, 3-dimensional reconstructions from medical scanners, and movies.

Applications include; removal of high-frequency noise in signals, and detecting and segmenting objects in images.

Learning Outcome

Knowledge of

  • Signal and image processing fundamentals.
  • Sampling, Sampling theorem, Fourier transform.
  • Convolution, linear and nonlinear filtering.
  • Image restoration, inverse filtering.
  • Image histograms.
  • Image segmentation.
  • Multiresolution processing.
  • Linear and non-linear spatial transformations of images.
  • Mathematical morphology.


Skills to

  • Apply basic signal processing methods to solve basic signal processing problems.

Competences to

  • Evaluate which signal/image processing methods and pipeline of methods is best suited for solving a given signal problem.
  • Understand the implications of theoretical theorems and being able to analyse real problems on that basis.


See Absalon for a list of course literature.

Academic qualifications equivalent to a BSc degree in computer science is recommended or mathematics skills equivalent to Linear Algebra, Mathematical Analysis and Probability Theory for Computer Scientists (MASD), and Modelling and Analysis of Data (MAD). Skills in computational thinking as obtained on PoP, DMA, LinAlgDat, and MASD or similar. As well as be proficient in Python programming as can be obtained in MASD and MAD or similar.
The course will be a mixture of lectures, pen-and-paper exercises, and programming exercises.
  • Category
  • Hours
  • Lectures
  • 32
  • Class Instruction
  • 24
  • Preparation
  • 64
  • Exercises
  • 86
  • Total
  • 206
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
Type of assessment details
Continuous assessment of 5-7 written assignments (of which 1-2 are individual and 4-5 are group assignments).

The final grade is based on an overall assessment of all assignments.
All aids allowed

For programming tasks specifically, the use of GitHub Copilot or similar AI-based programming tools is permitted. The finite list of allowed AI-tools will be announced in Absalon.

Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

A 25 minutes oral examination (including grading) without preparation in course curriculum

Criteria for exam assesment

See Learning Outcome.