Course Digital Imaging

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Responsible: Prof. Dr. Gregor Fischer

Course

Meets requirements of following modules(MID)

• in active programs
  • Ma MT2012 DBT

Course Organization

Version created 2013-04-25 VID 2 valid from WS 2012/13 valid to

Course identifiers Long name Digital Imaging CID F07_DBT CEID (exam identifier)

Contact hours per week (SWS) Lecture 2 Exercise (unsplit) Exercise (split) Lab 2 Project Seminar Tutorial(voluntary)

Total contact hours Lecture 30 Exercise (unsplit) Exercise (split) Lab 30 Project Seminar Tutorial (voluntary)

Max. capacity Exercise (unsplit) Exercise (split) Lab 18 Project Seminar

Total effort (hours): 150

Instruction language

• 70% German
• 30% English

Study Level

• Graduate

Prerequisites

• 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

Instructors

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

Supporting Scientific Staff

• tba

Transcipt Entry

Digital Imaging

Assessment

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

Total effort [hours]
oE	10

Frequency: 2-3/year

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Course components

Lecture/Exercise

Objectives

Contents

• 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

Type
fPS	excercise (on course and self study)

Contribution to course grade
fPS	not rated

Frequency: 1/year

Lab

Objectives

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

Type
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