Course Digital Imaging

Responsible: Prof. Dr. Gregor Fischer


Meets requirements of following modules(MID)

Course Organization

created 2013-04-25
valid from WS 2012/13
valid to
Course identifiers
Long name Digital Imaging
CEID (exam identifier)

Contact hours per week (SWS)
Lecture 2
Exercise (unsplit)
Exercise (split)
Lab 2
Total contact hours
Lecture 30
Exercise (unsplit)
Exercise (split)
Lab 30
Tutorial (voluntary)
Max. capacity
Exercise (unsplit)
Exercise (split)
Lab 18

Total effort (hours): 150

Instruction language

  • 70% German
  • 30% English

Study Level

  • Graduate


  • none

Textbooks, Recommended Reading

  • 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


  • Prof.Dr. Gregor Fischer
  • Prof.Dr. Dirk Poggemann

Supporting Scientific Staff

  • tba

Transcipt Entry

Digital Imaging


oE normal case (except on large numbers of assessments: wE

Total effort [hours]
oE 10

Frequency: 2-3/year

Course components



  • 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

Acquired 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

Additional Component Assessment

fPS excercise (on course and self study)

Contribution to course grade
fPS not rated

Frequency: 1/year



Acquired 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

Operational Competences
  • 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

Additional Component Assessment

fSC lab experiment (3h)
fTP 6 lab experiments (each 3h) per project team
oR presentation on fTP (20min per project team)

Contribution to course grade
fSC Attestation
fTP Attestation
oR prerequisite to course exam

Frequency: 1/year

Topic-Revision: r4 - 11 Jan 2016, GeneratedContent
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