DBT - Digital Imaging

PDF Course Catalog Deutsche Version: DBT

Version: 1 | Last Change: 08.10.2019 23:17 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben

Long name Digital Imaging
Approving CModule DBT_MaMT
Prof. Dr. Gregor Fischer
Professor Fakultät IME
Level Master
Semester in the year winter semester
Duration Semester
Hours in self-study 78
Prof. Dr. Gregor Fischer
Professor Fakultät IME
Requirements none
Language German
Separate final exam Yes
R.W.G. Hunt, The Reproduction of Color
M. Fairchild, Color Appearance Models, Wiley, 2nd ed.
G. C. Holst, T. S. Lomheim, CMOS/CCD Sensors and Camera Systems, SPIE
J. Nakamura, Image Sensors and Signal Processing for Digital Still Cameras, Taylor & Francis
Reinhard/Ward/Pattanaik/Debevec, High Dynamic Range Imaging, Elsevier 2010
R. Gonzales/R. Woods/Eddins, Digital Image Processing Using Matlab, Prentice Hall, 2004
W. Pratt, Digital Image Processing, Wiley, 4th ed., 2007
A. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1988
Final exam
Short project with final oral exam
Minimum standard
Working Matlab program
Oral presentation of the project objectives and the project results
Exam Type
Short project with final oral exam

Learning goals

Color Imaging
Color capturing with electronic sensors
Color detectors
Optical antialiasing filters
Color management for DSCs
ICC profiles computing with least squares fit
Testing color accuracy
Color appearance models
Multispectral Imaging
Spectral sensitivities estimation by means of a general method to stabilize an instable set of linear equations
Statistics of natural spectra (Principal Components Analysis)
Spectral stimulus estimation

HDR Imaging
HDR capturing technology
Contrast management
photo receptor model
unsharp masking
retinex algorithm
Automatic control

Imaging Methods
Automatic white balancing
Grey world approach
Dichromatic reflection model
MTF management
MTF measurement
filter design for MTF optimization and sharpening
Adaptive sharpening
Modelling of sensor noise
Locally adaptive smoothing filter
Wiener filtering
Bilateral filtering
Non-Local-Means filtering
Defect pixel / cluster correction

Describe the function and effects of different imaging methods

derive correction models for the image processing from the optical and electronic mechanisms

explain the application of basic mathematical tools for modelling and optimization of imaging methods
Expenditure classroom teaching
Type Attendance (h/Wk.)
Lecture 2
Exercises (whole course) 0
Exercises (shared course) 0
Tutorial (voluntary) 0
Special literature
Special requirements
Basics of the multivariate statistics, Principal Components Analysis (basic course mathematics)
Linear optimization methods (basic course mathematics)
Accompanying material
electronic slides as presented during lectures
electronic collection of excercises
Separate exam

Learning goals

analyse optical and electronic imaging characteristics

recognize and assess imaging defects

realize imaging methods by software programmin according to a given specification or scientific paper

measure optical and electronic imaging characteristics or defects

implement new imaging methods according to a given specification or scientific paper

optimize imaging methods by basic mathematical optimization methods

compare image quality of different imaging methods

document results
Expenditure classroom teaching
Type Attendance (h/Wk.)
Practical training 2
Tutorial (voluntary) 0
Special literature
Special requirements
Accompanying material
electronic description of lab-excercises
electronic developping tools for:
access to raw image data (Matlab)
image processing (Matlab)
digital camera simulation (Stanford's Imageval in Matlab)
Separate exam
Exam Type
working on practical scenarion (e.g. in a lab)
Protocol reports about lab exercises
Minimum standard
Reports for all lab excercises must be delivered in correct form with correct results

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