Course detail

Computational Photography

FIT-VYFAcad. year: 2025/2026

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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Martin Čadík, Ph.D.

Department

Department of Computer Graphics and Multimedia (UPGM)

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

  1. Project proposals
  2. Project assignments
  3. --
  4. --
  5. Consultations after the lecture - literature
  6. --
  7. Consultations after the lecture - implementation
  8. --
  9. Consultations after the lecture - testing
  10. --
  11. WRITTEN EXAM
  12. Finished implementations
  13. 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.

Aims

Methods of computational photography stand at the border of image processing, computer vision, computer graphics, physics, visual perception and other fields. The course offers the student a comprehensive view of this intersection, while a number of principles are demonstrated practically directly during the lectures (classical photography, HDR acquisition, tone mapping, image registration, spherical panoramic images, etc.). Students have the opportunity to participate in photo challenges and receive feedback from peers and lecturers. Previous knowledge of taught subjects on computer vision, graphics, or image processing is an advantage, but not required.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

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

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme MITAI Master's

    specialization NSEC , 0 year of study, summer semester, elective
    specialization NISY up to 2020/21 , 0 year of study, summer semester, elective
    specialization NNET , 0 year of study, summer semester, elective
    specialization NMAL , 0 year of study, summer semester, elective
    specialization NCPS , 0 year of study, summer semester, elective
    specialization NHPC , 0 year of study, summer semester, elective
    specialization NVER , 0 year of study, summer semester, elective
    specialization NIDE , 0 year of study, summer semester, elective
    specialization NISY , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NSPE , 0 year of study, summer semester, elective
    specialization NEMB , 0 year of study, summer semester, elective
    specialization NBIO , 0 year of study, summer semester, elective
    specialization NSEN , 0 year of study, summer semester, elective
    specialization NVIZ , 0 year of study, summer semester, elective
    specialization NGRI , 0 year of study, summer semester, elective
    specialization NADE , 0 year of study, summer semester, elective
    specialization NISD , 0 year of study, summer semester, elective
    specialization NMAT , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Martin Čadík, Ph.D.

Syllabus

  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

Project

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Martin Čadík, Ph.D.