Advanced Image Processing
This master course will focus on stochastic modelling techniques used in image processing. Coursework will constist of an oral exam, a project and homework exercises.
In short
All relevant course information, slides and materials are on dokeos.
Timetable
Lectures:
Labwork (TP) :
People
Instructors:
Sviatoslav Voloshynovskiy and Oleksiy Koval
Teaching Assistent
Course outline
- Algebra and stochastic modelling refresher
- Basic image processing algorithms
- Discrete Fourier Transform
- Multi-scale image representations
- Denoising
- Image restorations and zero knowledge deconvolution
Projects
You will do an image processing centric project. You may do this in a group or alone. You are free to choose your own project or select one of our suggestions.
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.
