Computational Image Processing
2014-2015 - IMT6131 - 5 ECTS

On the basis of

  • At least 30 ECTS credits university level mathematics including vector calculus and differential equations
  • Fundamental programming and algorithms
  • Fundamental image processing

Expected learning outcomes


  • The candidate is in the forefront of knowledge within the fields of selected computational image processing techniques
  • The candidate can evaluate the expediency and application of variational image processing methods and processes in research and development projects
  • The candidate has the ability to discuss and explain variational, wavelet and scale-space analytical methods


  • The candidate can formulate variational image processing problems using partial differential equations
  • The candidate can implement numerical solutions to variational image processing problems

General competence

  • The candidate has the ability to communicate and lead discussions on recent research about computational image processing methods
  • The candidate has the ability to evaluate and critique mechanisms for image modelling and representation


  • Variational calculus
  • Numerical solutions of PDEs
  • Total variation methods
  • Level-set representations
  • Wavelet and scalespace analysis
  • Image modelling and representation
  • Multiscale image processing
  • Applications to image processing problems such as denoising, deblurring, inpainting, segmentation, image difference, enhancement, gamut mapping, colour correction, demosaicing

Teaching Methods


Form(s) of Assessment


Form(s) of Assessment (additional text)

Candidates must provide one paper where the applicability of the topics of the course to her/his own thesis work is thoroughly discussed. The paper should be in the form of a scientific paper, include examples of the application of computational image processing methods, and preferably constitute a basis for a future publishable scientific paper.

Grading Scale


External/internal examiner

Evaluated by an internal examiner.

Re-sit examination


Examination support


Coursework Requirements

  • On a given topic, prepare and give one seminar consisting of an introductory lecture (1-2 hours) followed by a conducted group discussion.
  • Attend at least 75% of the lectures and seminars.

Teaching Materials

  • Chan, Tony and Jianhong Shen (2005). Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods . Society for Industrial Mathematics.
  • Selected papers (list announced at the beginning of the semester)

Additional information

In case there will be less than 5 candidates that apply for the course, it will be at the discretion of the course responsible whether the course will be offered or not and, if yes, in which form.