Course Manual 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 |
| Valid from | winter semester 2020/21 |
| 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 |
Final exam
| Details | Short project with final oral exam |
|---|---|
| Minimum standard | Working Matlab program Oral presentation of the project objectives and the project results |
| Exam Type | EN mündliche Prüfung, strukturierte Befragung |
Learning goals
| Goal type | Description |
|---|---|
| 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 |
| Knowledge | HDR Imaging HDR capturing technology Contrast management photo receptor model unsharp masking retinex algorithm Automatic control |
| Knowledge | 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 |
| Skills | derive correction models for the image processing from the optical and electronic mechanisms |
| Skills | 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 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 | No |
Learning goals
| Goal type | Description |
|---|---|
| Skills | analyse optical and electronic imaging characteristics |
| Skills | recognize and assess imaging defects |
| Skills | realize imaging methods by software programmin according to a given specification or scientific paper |
| Skills | measure optical and electronic imaging characteristics or defects |
| Skills | implement new imaging methods according to a given specification or scientific paper |
| Skills | optimize imaging methods by basic mathematical optimization methods |
| Skills | compare image quality of different imaging methods |
| Skills | document results |
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Practical training | 2 |
| Tutorial (voluntary) | 0 |
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) |
|---|---|
| Separate exam | Yes |
Separate exam
| Exam Type | EN praxisnahes Szenario bearbeiten (z.B. im Praktikum) |
|---|---|
| Details | 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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