Course details

Computational Photography

VYF Acad. year 2024/2025 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.

Guarantor

Čadík Martin, doc. Ing., Ph.D. (DCGM)

Language of instruction

Czech

Completion

Classified Credit (written)

Time span

  • 26 hrs lectures
  • 26 hrs projects

Assessment points

  • 40 pts mid-term test
  • 60 pts projects

Department

Department of Computer Graphics and Multimedia (UPGM)

Lecturer

Čadík Martin, doc. Ing., Ph.D. (DCGM)

Instructor

Čadík Martin, doc. Ing., Ph.D. (DCGM)

Learning objectives

The aim is to introduce computational photography methods (http://cphoto.fit.vutbr.cz/) and to get acquainted with the principles of mathematics and computer science in the field.

Fundamental literature

  • Radke, R.: Computer Vision for Visual Effects. Cambridge university press.  2013.
  • Szeliski, R.: Computer Vision: Algorithms and Applications, Springer. 2010.

  • Shirley, P., Marschner, S.: Fundamentals of Computer Graphics. CRC Press. 2009.

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
  6. WRITTEN EXAM
  7. Finished implementations
  8. Presentations of assignments, final reports


Course inclusion in study plans

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