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.
Timetable
Lectures: 16:00 - 18:00, thursday, Battelle 404-407
Labwork (TP) : 8:00 - 10:00, friday, Battelle 314-315
People
Instructors:
Teaching Assistent
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
Books
- 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.
Prerequisites
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.
