Prerequisite(s)

The student has to be working on a thesis.

On the basis of

• At least 20 ECTS credits university level mathematics
• Fundamental programming
• Algorithms
• Fundamental image processing

Expected learning outcomes

Upon completion of the course, the students should be able to:

• Explain the fundamental methods taught in the course (see topic list)
• Apply the methods to advanced image processing tasks (see topic list)
• Analyse and discuss the suitability of variational, PDE, and stochastic methods to image processing tasks related to their own thesis.

Topic(s)

• Variational calculus
• Partial differential equations (PDEs)
• Numerical solutions of PDEs
• Level-set representations
• Fundamentals of statistical mechanics
• Wavelet and scalespace analysis
• Image modelling and representation
• Applications to:
  • Image denoising
  • Image deblurring
  • Image inpainting
  • Image segmentation

Teaching Methods

Lectures
Meeting(s)/Seminar(s)
Tutoring

Form(s) of Assessment

Other

Form(s) of Assessment (additional text)

Write a short paper (3-5 pages) discussing the suitability of the course topics to the student's own thesis.

Grading Scale

Passes / Failed

External/internal examiner

Evaluated by the course responsible.

Re-sit examination

None

Coursework Requirements

• On a given topic, prepare and give one seminar consisting of an introductory lecture (1-2 hours) followed by a 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.