Space-time imaging
Published in Elsevier, 2026
Recommended citation: James Skowronek, Felipe Guzmán, Nelson Díaz, Esteban Vera and David Brady, “Space-time imaging,” Elsevier, Vol. 54, pp. 119-145, 2026. [DOI].
This chapter provides a survey of space-time imaging, focusing on the evolution from traditional frame-based video to modern computational imaging. It addresses the fundamental challenge of capturing high-dimensional dynamic scenes under the constraints of system and sensor bandwidth, and data volume. The discussion reviews video sensors, snapshot compressive video (SCV), and ultrafast imaging. Ultimately, the chapter highlights a fundamental shift toward the co-design of optics, electronics, and algorithms to efficiently sample the plenoptic function.
Cite
@incollection{SKOWRONEK2026119,
title = {Chapter 5 - Space-time imaging},
editor = {Kumar Vijay Mishra and Gonzalo R. Arce and Arni S.R. {Srinivasa Rao}},
series = {Handbook of Statistics},
publisher = {Elsevier},
volume = {54},
pages = {119-145},
year = {2026},
booktitle = {Multidimensional Signal Processing},
issn = {0169-7161},
doi = {https://doi.org/10.1016/bs.host.2026.03.004},
url = {https://www.sciencedirect.com/science/article/pii/S0169716126000131},
author = {James Skowronek and Felipe Guzmán and Nelson Díaz and Esteban Vera and David Brady},
keywords = {Computational imaging, Snapshot compressive imaging (SCI), Snapshot compressive video (SCV), Coded aperture, Coded illumination, Coded exposure, Ultrafast imaging, Event cameras, Array cameras, Deep learning},
abstract = {This chapter provides a survey of space-time imaging, focusing on the evolution from traditional frame-based video to modern computational imaging. It addresses the fundamental challenge of capturing high-dimensional dynamic scenes under the constraints of system and sensor bandwidth, and data volume. The discussion reviews video sensors, snapshot compressive video (SCV), and ultrafast imaging. Ultimately, the chapter highlights a fundamental shift toward the co-design of optics, electronics, and algorithms to efficiently sample the plenoptic function.}
}