Image Processing in Physics
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2017
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
|available module versions|
|SS 2019||WS 2018/9||SS 2018||WS 2017/8||SS 2017||WS 2013/4|
PH2181 is a semester module in English or German language at Master’s level which is offered every semester.
This Module is included in the following catalogues within the study programs in physics.
- Specific catalogue of special courses for Biophysics
- Specific catalogue of special courses for Applied and Engineering Physics
- Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
- Elective Modules Natural Sciences in the Master Program Matter to Life
- Complementary catalogue of special courses for condensed matter physics
- Complementary catalogue of special courses for nuclear, particle, and astrophysics
If not stated otherwise for export to a non-physics program the student workload is given in the following table.
|Total workload||Contact hours||Credits (ECTS)|
|150 h||75 h||5 CP|
Responsible coordinator of the module PH2181 in the version of SS 2017 was Julia Herzen.
Content, Learning Outcome and Preconditions
This module covers a wide range of basic and advanced techniques used for image processing and image reconstruction, with a special focus on physical science applications. Following a problem-solving philosophy, the module motivates all techniques and fundamental concepts with problems drawn from real-life applications.
The topics covered may be roughly divided into three parts, one focusing on basics of image processing and data analysis, one part based on image formation in optical devices, its requirements and limitations, and one part on more abstract algorithmic approaches to data analysis and optimization.
The module gives a rather broad overview over recurring topics in all fields of imaging, focusing more on an understanding of underlying principles than on rigorous mathematical derivations.
After participation to the Module, the student:
- Will know the fundamentals of numerical analysis.
- Will know the basics of the standard imaging analysis methods in research and the industry.
- Will have a broad overview of the state of the art in many fields of imaging physics.
- Will be able to solve typical data analysis problems occurring in experimental physics and engineering.
The exercises will be given in python. No specific computer science or programming knowledge is required. The practicals offered together with the course also do not require any specific knowledge.
Some basic mathematics knowledge is expected: this includes basics of calculus, statistics, linear algebra & functional analysis (matrices, vectorspaces, bases, Fourier transformation, etc). In general the mathematical content will not be the focus of the course.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Image Processing in Physics||Achterhold, K. Herzen, J.||
Thu, 12:00–14:00, MW 2050
|UE||1||Exercise to Image Processing in Physics||
Responsible/Coordination: Achterhold, K.
|dates in groups|
Learning and Teaching Methods
Each class will address a specific technique, yet all will be linked by recurrent essential topics including for example Fourier analysis, linear algebra, iterative techniques, maximum likelihood and convex optimization.
Together with each class, an exercise lesson is offered, where the student can directly apply and test the studied method. Typically this will involve writing a few lines of code (<10) in python to complete an existing program.
Will be defined in class
Description of exams and course work
In an oral exam the learning outcome is tested using comprehension questions and sample problems.
In accordance with §12 (8) APSO the exam can be done as a written test. In this case the time duration is 60 minutes.
The exam may be repeated at the end of the semester.