Course detail
Elements of Digital Signal and Image Processing
FEKT-MEDSAcad. year: 2018/2019
The course is intended as an introduction to signal and image processing and analysis in the English language.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
- interpreting the fundamental knowledge, concepts and their relationships in the field of signal and image processing,
- describing the basic methods in this area,
- using English terminology in the area, and reading the respective literature in English with understanding.
Prerequisites
- basic university mathematics, including the complex integral transforms
- introduction to continuous-time system theory
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
2. Digital signals - sampling and reconstruction, discrete spectra.
3. Principles and properties of digital linear filtering - FIR filters.
4. Principles and properties of digital linear filtering - IIR filters.
5. Noise suppression and signal restoration – averaging methods, optimal filtering.
6. Discrete correlation analysis
7. Discrete spectral analysis (deterministic signals)
8. Discrete spectral analysis (stochastic signals)
9. Basics of analogue image representation, two-dimensional signals and systems.
10. Discrete and digital images, 2D discrete transforms.
11. Basic 2D image processing operators, contrast and colour transforms.
12. Image enhancement - sharpening, noise suppression.
13. Introduction to reconstruction of images from tomographic projections.
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
J.Jan: Digital Signal Filtering, Analysis and Restoration, IEE London (UK) 2000, ISBN 0 85296 760 8
J.Jan: Medical Image Processing, Reconstruction and Restoration. CRC 2006
Recommended reading
Classification of course in study plans
- Programme EEKR-M Master's
branch M-KAM , 2 year of study, summer semester, elective general
branch M-EEN , 2 year of study, summer semester, elective general
branch M-MEL , 2 year of study, summer semester, elective general
branch M-EVM , 2 year of study, summer semester, elective general
branch M-EST , 2 year of study, summer semester, elective general
branch M-SVE , 2 year of study, summer semester, elective general
branch M-TIT , 2 year of study, summer semester, elective general
branch M-BEI , 2 year of study, summer semester, elective general
branch M-KAM , 1 year of study, summer semester, elective general
branch M-EEN , 1 year of study, summer semester, elective general
branch M-MEL , 1 year of study, summer semester, elective general
branch M-EVM , 1 year of study, summer semester, elective general
branch M-EST , 1 year of study, summer semester, elective general
branch M-SVE , 1 year of study, summer semester, elective general
branch M-TIT , 1 year of study, summer semester, elective general
branch M-BEI , 1 year of study, summer semester, elective general - Programme EEKR-CZV lifelong learning
branch EE-FLE , 1 year of study, summer semester, elective general
- Programme EECC-MN Master's
branch MN-BEI , 1 year of study, summer semester, elective general
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
time and frequency domains, deterministic and stochastic signals. Digital
signals - sampling and reconstruction, discrete spectra. Principles and
properties of digital linear filtering - FIR and IIR filters. Noise
suppression and signal restoration - averaging, optimal filtering.
Discrete correlation analysis and spectral analysis.
Basics of digital image representation, two-dimensional signals and
systems. Basic image processing operators, image enhancement - sharpening,
noise suppression, contrast and colour transforms.