Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Autumn 2024
Content and learning outcomes
Course contents
This course introduces the principles of digital image and video processing, discusses current image and video processing technology, and provides hands-on experience with image/video processing and communication methods. The course includes topics on image filtering and restoration, image transform algorithms, multiresolution image processing, image matching and segmentation techniques, as well as image and video compression.
Intended learning outcomes
After passing this course, participants should be able to
describe and use the principles of digital image and video processing to develop image processing algorithms,
develop image processing algorithms for image filtering and restoration, image transformation and multiresolution processing, image and video compression, as well as image matching and segmentation,
implement (for example with MatLab) and assess the developed image processing algorithms,
explain algorithm design choices using the principles of digital image/video processing,
develop image processing algorithms for a given practical image/video processing problem
analyze given image/video processing problems, identify and explain the challenges, propose possible solutions, and explain the chosen algorithm design.
To achive higher grades, participants should also be able to
solve more advanced problems in all areas mentioned above.
Learning activities
Individual preparation assignments, peer reviews, group projects, exercises, lectures.
Detailed plan
Learning activities
Content
Preparations
Lectures
Exercises
Individual preparation assignments
Prepare before exercise
Peer reviews of preparation assignments
During exercise session or afterwards, if exercise session cannot be attended
3 group projects
Group of 2-3 students; submit one report per project and group
Preparations before course start
Recommended prerequisites
EQ1220 Signal Theory or equivalent
Literature
Content is covered by lecture and exercise material.
For further reading, the following book is helpful: R. C. Gonzales, R.E. Woods, “Digital Image Processing,” Prentice-Hall.
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
INL1 - Assignment, 1.5 credits, Grading scale: P, F
PRO1 - Course projects, 3.0 credits, Grading scale: A, B, C, D, E, FX, F
TENA - Written exam, 3.0 credits, Grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
The section below is not retrieved from the course syllabus:
Course projects will contribute to the final grade. In general, projects and written exam contribute equally to the final grade. The examiner reserves the right to adjust the final weighting at the end of the course.
Ethical approach
All members of a group are responsible for the group's work.
In any assessment, every student shall honestly disclose any help received and sources used.
In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.