Computer Engineering MA, Signal and Image Processing, 6 credits

Aim

The course aims to provide a deep understanding of both fundamental and advanced methods within signal and image processing, by equipping students with the theoretical knowledge and practical skills required to successfully apply these methods in areas such as computer vision, image analysis, multidimensional visual representation and compression, advanced image processing, as well as applications within machine learning for visual media.

Course objectives

Upon completing the course, the student will be able to:

• describe and explain the theoretical fundamental principles within signal and image processing
• apply the fundamental principles of signal and image processing in both traditional methods and modern AI-based systems.
• select and apply appropriate signal and image analysis techniques on specific problems with visual media.
• describe and explain algorithms within signal and image processing based on mathematical foundations.
• design and implement filters for signals and images in both time, space, and frequency domains.
• evaluate and critically analyze the performances of different signal and image processing algorithms, using tools and programming languages relevant to the area.

Content

• Introduction to signal and image processing: signal and system representations (time/frequency/space), sampling, quantization, image formats, convolution, Hilbert spaces, transforms, quality metrics, and applications.
• Time-domain analysis and digital filter design: FIR/IIR, impulse response, computational complexity.
• Frequency-domain analysis and filter design: Fourier analysis, LP/HP/BP/BS, spectrum, spectrogram.
• Spatial-domain analysis: filter kernels, spatial filters for sharpening, noise reduction, histograms, contrast enhancement, color space.
• Introduction to how signal and image processing are applied in visual AI.
• Implementation examples for real-world problems in signal and image processing, as well as analysis of scientific articles.

Entry requirements

Computer Engineering, 60 credits including object oriented programming, 15 credits; Mathematical subjects, 30 credits including probability theory and statistics, and linear algebra.

Selection rules and procedures

The selection process is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The course is taught using lectures, laboratory sessions, seminars, and finally a written exam. The large part of the course is with limited supervision, where the student is assumed to work on lecture material, and practical work or project.

Examination form

L101: Laboratory Exercises, 1.5 Credits
Grade scale: Two-grade scale

T101: Exam, 4.5 Credits
Grade scale: Seven-grade scale, A-F o Fx

Link to subject-specific grading criteria: www.miun.se/gradingcriteria.



The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.



If examination on campus cannot be conducted according to decision by the vice-chancellor, or whom he delegated the right to, the following applies: [Written Exam T101], will be replaced with two parts, online examination and follow-up. Within three weeks of the online examination, a selection of students will be contacted and asked questions regarding the examination. The follow-up consists of questions concerning the execution of the on-line exam and the answers that the student have submitted.

Examination restrictions

Students registered on this version of the syllabus have the right to be examined 3 times within 1 year according to specified examination forms. After that, the examination form applies according to the most recent version of the syllabus.

Grading system

Seven-grade scale, A-F o Fx