Image Processing in Physics
Module version of SS 2022
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|WS 2022/3||SS 2022||SS 2021||WS 2020/1||SS 2020||WS 2019/20||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||45 h||5 CP|
Responsible coordinator of the module PH2181 in the version of SS 2022 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.
The module covers the following topics:
- Image processing in spatial domain
- Image processing in Fourier domain
- Sampling, interpolation & pixel representations
- Resolution & Noise
- Wave propagation
- Wavelets and windowed Fourier transform
- Optimization (Constrained + Least Squares)
- Phase-contrast Imaging
- Grating-based imaging
After successful module participation the student is able to:
- apply the basic principles of the discussed image processing techniques (filtering in spatial and in fourier domain, interpolation, segmentation, noise and resolution analysis, tomographic reconstruction, wave propagation and phase retrieval).
- identify the imaging technique to address specific biomedical questions.
- analyze images with respect to the used image-processing technique with its advantages and disadvantages.
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. Mathematical knowledge drom the Physucs Bachelor is sufficient.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||2||Image Processing in Physics||Achterhold, K. Herzen, J.||
singular or moved dates
|UE||1||Exercise to Image Processing in Physics||
Responsible/Coordination: Achterhold, K.
singular or moved dates
and 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.
During the lecture the learning content is presented. After the lecture there is time for discussion. Questions in form of a quiz give the opportunity to apply what has been learned.
Additional notes, textbook recommendations and literature references are provided for further deepening of the learning content
Power point slides filled in class
- Rafael Gonzales, Richard Woods, “Digital Image Processing”, 3rd ed.
- Bernd Jähne, “Digitale Bildverarbeitung und Bildgewinnung”, 7th ed.
- Lipson, Lipson, Tannhauser, “Optik” (German) 3rd ed., [english edition “Optical physics” available as “Vollansicht” from TUM library]
- Bishop, “Pattern Recognition and Machine Learning” 1st ed.
- Max Born, Emil Wolf, “Principles of Optics”, 7th ed.
- Joseph Goodman, “Introduction to Fourier Optics”, 3rd ed.
- William Pratt, “Digital Image Processing” http://onlinelibrary.wiley.com/book/10.1002/0470097434
- Stephen Smith, “The Scientist and Engineer’s Guide To Digital Signal Processing” http://www.dspguide.com
- Roger Easton, “Fourier Methods in Imaging” http://onlinelibrary.wiley.com/book/10.1002/9780470660102
- Gabriel Cristobal, “Optical and Digital Image Processing: Fundamentals and Applications” http://onlinelibrary.wiley.com/book/10.1002/9783527635245
- Tinku Acharya, “Image Processing: Principles and Applications” http://onlinelibrary.wiley.com/book/10.1002/0471745790
- Avinash Kak, Malcolm Slaney, “Principles of computerized tomographic imaging” http://www.slaney.org/pct/
- Richard Szileski, “Computer Vision: Algorithms and Applications” http://szeliski.org/Book/
- David Barber, “Bayesian Reasoning and Machine Learning” http://www.cs.ucl.ac.uk/staff/d.barber/brml/
- Simon J.D. Prince, “Computer Vision: Models, Learning, and Inference” http://www.computervisionmodels.com/
- Trevor Hastie “The Elements Of Statistical Learning”, 2nd ed. http://www-stat.stanford.edu/~tibs/ElemStatLearn/
- Otmar Scherzer. “Handbook of Mathematical Methods in imaging” http://link.springer.com/book/10.1007/978-0-387-92920-0
Description of exams and course work
There will be a written exam of 60 minutes duration. Therein the achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using comprehension questions and case studies.
For example an assignment in the exam might be:
- Explain the concept of spatial frequencies and indicate them in an exemplary image.
- How do you get 3D volumes from 2D projections?
- What is the difference between standard attenuation-based and phase-contrast imaging?
Participation in the exercise classes is strongly recommended since the exercises prepare for the problems of the exam and rehearse the specific competencies.
The exam may be repeated at the end of the semester.