Course details

Computational Photography

VYF Acad. year 2016/2017 Summer semester 5 credits

Current academic year

Current digital cameras almost completely surpass traditional photography. They do not only capture light, they in fact compute pictures. That said, there is practically no image that would not be computationally processed to some extent today. Visual computing is ubiquitous. Unfortunately, images taken by amateur photographers often lack the qualities of professional photos and some image editing is necessary. Computational photography (CP) develops methods to enhance or extend the capabilities of the current digital imaging chain.


Language of instruction



Classified Credit (written)

Time span

26 hrs lectures, 26 hrs projects

Assessment points

40 mid-term test, 60 projects



Course Web Pages

Learning objectives

The aim is to introduce computational photography methods ( and to get acquainted with the principles of mathematics and computer science in the field.

Fundamental literature

  • Shirley, P., Marschner, S.: Fundamentals of Computer Graphics. CRC Press. 2009.
  • Szeliski, R.: Computer Vision: Algorithms and Applications, Springer. 2010.
  • Bradski, G. and Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly. 2008.

Syllabus of lectures

  1. introduction to CP, light and color (slides, projects, final report template)
  2. photography, optics, physics, sensors, noise (slides)
  3. visual perception, natural image statistics (slides)
  4. image blending (slides)
  5. Color, color spaces, color transfer, color-to-grayscale image conversions (slides)
  6. High dynamic range (HDR) imaging - acquisition, storage and display (slides, HDR file)
  7. High dynamic range (HDR) imaging - tone mapping, inverse tone mapping (slides)
  8. Image registration for computational photography (slides, spherical /360x180/ panorama)
  9. Computational illumination, dual photography, illumination changes (slides)
  10. Image and video quality metrics (slides)
  11. Omnidirectional camera, lightfields, synthetic aperture (slides)
  12. Non-photorealistic camera, computational aesthetics (slides)
  13. Computational video, GraphCuts, presentations of projects

Progress assessment

  1. Project proposals
  2. Project assignments
  3. Consultations after the lecture - literature
  4. Consultations after the lecture - implementation
  5. Consultations after the lecture - testing
  7. Finished implementations
  8. Presentations of assignments, final reports

Exam prerequisites

At least 50 points must be obtained, while the minimal score from the test is 16 points, the minimal score from the project is 24 points.

Course inclusion in study plans

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