NDAA09027U  Signal and Image Processing (SIP)

Volume 2017/2018

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 change with 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 sound, detecting and segmenting objects in images, and reconstruction of 3-dimensional computed tomography images (CT) from X-ray images.

Learning Outcome


  • 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.



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


  • 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 when the course is set up.

The course will be a mixture of lectures, pen-and-paper exercises, and programming exercises.
Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Continuous assessment
Continuous evaluation of 7 written assignments.
The assignments must be individually approved. The final grade is based on an overall assessment.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Oral examination (25 minutes including grading) in course curriculum without preparation. Internal grading using the 7-point grading scale.

Criteria for exam assesment

See Learning Outcome.


  • Category
  • Hours
  • Lectures
  • 38
  • Theory exercises
  • 12
  • Practical exercises
  • 12
  • Preparation
  • 16
  • Project work
  • 128
  • Total
  • 206