Course
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 |
| Responsible |
Prof. Dr. Gregor Fischer
Professor Fakultät IME |
| Level | Master |
| Semester in the year | winter semester |
| Duration | Semester |
| Hours in self-study | 78 |
| ECTS | 5 |
| Professors |
Prof. Dr. Gregor Fischer
Professor Fakultät IME |
| Requirements | none |
| Language | German |
| Separate final exam | Yes |
Literature
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
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
Details
Short project with final oral examMinimum standard
Working Matlab programOral presentation of the project objectives and the project results
Exam Type
Short project with final oral examLearning goals
Knowledge
Color Imaging
Color capturing with electronic sensors
Color detectors
Demosaicking
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
Color-by-Correlation
Dichromatic reflection model
MTF management
MTF measurement
filter design for MTF optimization and sharpening
Adaptive sharpening
Denoising
Modelling of sensor noise
Locally adaptive smoothing filter
Wiener filtering
Bilateral filtering
Non-Local-Means filtering
Defect pixel / cluster correction
Color capturing with electronic sensors
Color detectors
Demosaicking
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
Color-by-Correlation
Dichromatic reflection model
MTF management
MTF measurement
filter design for MTF optimization and sharpening
Adaptive sharpening
Denoising
Modelling of sensor noise
Locally adaptive smoothing filter
Wiener filtering
Bilateral filtering
Non-Local-Means filtering
Defect pixel / cluster correction
Skills
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
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
keine/none
Special requirements
Basics of the multivariate statistics, Principal Components Analysis (basic course mathematics)
Linear optimization methods (basic course mathematics)
Linear optimization methods (basic course mathematics)
Accompanying material
electronic slides as presented during lectures
electronic collection of excercises
electronic collection of excercises
Separate exam
none
Learning goals
Skills
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
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
keine/none
Special requirements
none
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)
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)Details
Protocol reports about lab exercisesMinimum standard
Reports for all lab excercises must be delivered in correct form with correct results
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