Module MA5032-KP05

Duration

1 Semester

Turnus of offer

each winter semester

Credit points

5

Course of studies, specific fields and terms:

• Bachelor CLS 2023, compulsory, mathematics
• Bachelor CLS 2016, optional subject, mathematics
• Master CLS 2016, optional subject, mathematics

Classes and lectures:

• Numerical Methods for Image Computing (exercise, 1 SWS)
• Numerical Methods for Image Computing (lecture, 2 SWS)

Workload:

• 30 hours work on project
• 10 hours exam preparation
• 45 hours in-classroom work
• 65 hours private studies and exercises

Contents of teaching:

• Imaging process an imaging modalities
• Grids and image representation
• Image operators and finite differences
• Stationary partial differential equations in image processing
• Image and video compression
• Variation formulation and statistical interpretation
• Correctness and regularisation

Qualification-goals/Competencies:

• The students are familiar with fundamental numerical concepts in image computing.
• They have experience in realizing practical solutions.
• They can implement numerical algorithms on a computer.
• Interdisciplinary qualifications:
• Students have advanced skills in modeling.
• They can translate theoretical concepts into practical solutions.
• They are experienced in implementation.
• They can think abstractly about practical problems.

Grading through:

• Written or oral exam as announced by the examiner

Responsible for this module:

• Prof. Dr. rer. nat. Jan Modersitzki

Teacher:

• Institute of Mathematics and Image Computing
• Prof. Dr. rer. nat. Jan Modersitzki
• Prof. Dr. rer. nat. Jan Lellmann

Literature:

• Bredies, Lorenz : Mathematische Bildverarbeitung Springer, 2010
• Gonzalez, Woods : Digital Image Processing Prentice Hall, 2007
• Hackbusch : Iterative Lösung großer schwachbesetzter Systeme Teubner, 1993
• Briggs : A Multigrid Tutorial SIAM, 2000
• Nocedal, Wright : Numerical Optimization Springer, 2006

Language:

• German and English skills required

Notes:

Last Updated:

19.09.2025