Yoav Y. Schechner: Research


Computational Imaging on the Electric Grid

Night beats with alternating current (AC) illumination. By passively sensing this beat, we reveal new scene information which includes: the type of bulbs in the scene, the phases of the electric grid up to city scale, and the light transport matrix. This information yields unmixing of reflections and semi-reflections, nocturnal high dynamic range, and scene rendering with bulbs not observed during acquisition. The latter is facilitated by a database of bulb response functions for a range of sources, which we collected and provide (DELIGHT). To do all this, we introduce a novel coded-exposure high-dynamic-range imaging technique, specifically designed to operate on the grid’s AC lighting. This camera system, which we built and demonstrate, is the ACam.


  1. Mark Sheinin, Yoav Y. Schechner and Kiriakos. N. Kutulakos, Computational imaging on the electric grid,” Proc. IEEE CVPR (2017) Oral, Best Student Paper Award.
  2. Mark Sheinin, Yoav Y. Schechner and Kiriakos. N. Kutulakos, Computational imaging on the electric grid: Supplementary material,” Supplemental document in Proc. IEEE CVPR (2017), describing the DELIGHT database and some technical aspects.


  1. A narrated presentation in YouTube, intended for the wider audience.
  2. Computational Imaging on the Electric Grid, A presentation with embedded videos and graphics, intended for the wider audience (61 Mb, PowerPoint).
  3. A video is supplemental to the CVPR official publication. (13.9 Mb, mp4).


DELIGHT is a Database of Electric LIGHTs. It contains bulb response functions ahnd chromaticities, as described in our paper. Available for non-commercial use. You can use it if you clearly acknowledge the source by citing "Computational imaging on the electric grid" detailed above, in your work.
  1. The database DELIGHT described in the CVPR'17 paper Computational imaging on the electric grid. (74 Mb)

Related Research

  1. Multiplex Illumination
  2. Hypertemporal Imaging of NYC Grid Dynamics, Bianco et. al.
  3. Multiplexed Fluorescence Unmixing
  4. Optimized Poisson Compressed Sensing Matrix
  5. Semi-Reflections: Polarization-based Separation
  6. Blind Source Separation
Hallway scene: click to see digital separation to components and relighting.