Advanced Image Processing

This master course will focus on stochastic modelling techniques used in image processing. Coursework will constist of an oral exam and homework exercises.

In short

All relevant course information, slides and the course reader are on dokeos.


Lectures: 16:00 - 18:00, thursday, Battelle 404-407

Labwork (TP) : 8:00 - 10:00, friday, Battelle 314-315



Sviatoslav Voloshynovskiy

Teaching Assistent

Maurits Diephuis

Course outline

  • Algebra and stochastic modelling refresher
  • Human vision system
  • Multi-scale image representations
  • Stochastic image models in the transform domains
  • Applications
  • Image denoising
  • Image restorations and zero knowledge deconvolution

Supplementary Materials


  • A. K. Jain , Fundamentals of Digital Image Processing, Prentice-Hall, 1989.
  • A. Bovik, Handbook of Image & Video Processing, Academic Press, 2000.
  • H. Stark and J. W. Woods, Probability, Random Processes, and Estimation Theory for Engineers, Prentice-Hall, 1994.
  • H. V. Poor, An Introduction to Signal Detection and Estimation, 2nd Ed., Springer-Verlag, 1994.
  • M. Vetterli and J. Kovacevic, Wavelets and Subband Coding, Prentice-Hall, 1995.


You are highly recommended to refresh your knowledge on probability and statistics as of basic digital image processing prior to this class.

Practical work can be done in a language of your choice. We have a preference for Matlab or Python.  Futhermore, we suggest that you refresh your programming and image handling skills.