Course details

Computational Photography

VYF Acad. year 2021/2022 Summer semester 5 credits

Current academic year

Course is not open in this 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.


Course coordinator

Language of instruction



Classified Credit (written)

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 40 pts mid-term test
  • 60 pts 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.

Why is the course taught

Computational photography techniques are placed at boundaries of image processing, computer vision, physics, visual perception and other fields. The course is offering a holistic view of this intersection, while many principles are demonstrated practically during lectures (photography, HDR acquisition, tone mapping, image registration, spherical panoramic imaging, etc.). Students may take part in photographic challenges and get a valuable feedback from their colleagues and tutors. Prior knowledge of computer vision, graphics or image processing is beneficial, but not required.

Study literature

  • Szeliski, R.: Computer Vision: Algorithms and Applications, Springer. 2010.
  • Radke, R.: Computer Vision for Visual Effects. Cambridge university press.  2013.
  • Shirley, P., Marschner, S.: Fundamentals of Computer Graphics. CRC Press. 2009.

Fundamental literature

  • Radke, R.: Computer Vision for Visual Effects. Cambridge university press.  2013.

Syllabus of lectures

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

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

It is obligatory to be present at the written exam, submit the project including textual report and oral presentation. 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. During the term, one can get bonus points in practical photography challenges.

Course inclusion in study plans

Back to top