Abstract
Photography has been striving to capture an ever increasing amount of visual information in a single image. Digital sensors, however, are limited to recording a small subset of the desired information at each pixel. A common approach to overcoming the limitations of sensing hardware is the optical multiplexing of high-dimensional data into a photograph. While this is a well-studied topic for imaging with color filter arrays, we develop a mathematical framework that generalizes multiplexed imaging to all dimensions of the plenoptic function. This framework unifies a wide variety of existing approaches to analyze and reconstruct multiplexed data in either the spatial or the frequency domain. We demonstrate many practical applications of our framework including high-quality light field reconstruction, the first comparative noise analysis of light field attenuation masks, and an analysis of aliasing in multiplexing applications.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 384-400 |
| Number of pages | 17 |
| Journal | International Journal of Computer Vision |
| Volume | 101 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 2013 |
| Externally published | Yes |
Bibliographical note
Funding Information:Acknowledgments We thank Dolby Canada for their support and the anonymous reviewers for their insightful feedback. Gordon Wetzstein was supported by a UBC Four Year Fellowship, an NSERC Postdoctoral Fellowship, and the DARPA SCENICC program. Wolfgang Heidrich was supported under the Dolby Research Chair in Computer Science at UBC. Ivo Ihrke was supported by a Feodor-Lynen Fellowship of the Humboldt Foundation, Germany.
Keywords
- Computational photography
- Light fields
- Optical multiplexing
- Plenoptic function
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Artificial Intelligence