Vorlesung: Computational Photography


  • Dozent(en):
  • Beginn: April 18, 2016
  • Zeiten: Montags 14:15 s.t., LBH - Hörsaal III.03. Achtung: Erster Termin beginnt um 14:30.
  • Veranstaltungsnummer: MA-INF 2214
  • Studiengang: Master



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About the course

Although the digital photography industry is expanding rapidly, most digital cameras still look and feel like film cameras, and they offer roughly the same set of features and controls. However, as sensors and in-camera processing systems improve, cameras and mobile devices are beginning to offer capabilities that film cameras never had. Among these are the ability to refocus photographs after they are taken (see the example above), or to combine views taken with different camera settings, aim, or placement. Equally exciting are new technologies for creating efficient, controllable illumination. Future "flashbulbs" may be pulsed LEDs or video projectors, with the ability to selectively illuminate objects, recolor the scene, or extract shape information. These developments force us to relax our notion of what constitutes "a photograph." They also blur the distinction between photography and scene modeling. These changes will lead to new photographic techniques, new scientific tools, and possibly new art forms.

In this course, we will survey the converging technologies of digital photography, computational imaging, and image-based rendering, and we will explore the new imaging modalities that they enable.


This is an advanced course for students with background in computer graphics or computer vision. The content is reflecting our conviction that successful researchers in this area must understand both the algorithms and the underlying technologies. The lectures may be accompanied by readings from textbooks or the research literature. These readings will be handed out in class or placed on the course web site. Students are expected to:

  1. attend the lectures, and participate in class discussions
  2. complete the practical assignments (including a course project to be prepared and presented in teams).

A written or oral exam will conclude the course.

Tentative schedule

1Mon, April 18Introduction
2Mon, April 25Sensors
3Mon, May 2Optics
4Mon, May 9Signal processing basics
5Mon, May 16No lecture (holiday)
6Mon, May 23Inverse problems: deconvolution+tomography
7Mon, May 30Light fields
8Mon, June 6Reflectance fields and materials
9Mon, June 13Light fields
10Mon, June 20Computational illumination and display
11Mon, June 27Graphics-based vision
12Mon, July 4 Computational transient imaging
13Mon, July 11Research update
14Mon, July 18Project presentations