Illumination-Based Color Reconstruction for the Dynamic Vision Sensor

Khen Cohen, Omer Hershko, Homer Levy, David Mendlovic, Dan Raviv*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This work demonstrates a novel, state-of-the-art method to reconstruct colored images via the dynamic vision sensor (DVS). The DVS is an image sensor that indicates only a binary change in brightness, with no information about the captured wavelength (color) or intensity level. However, the reconstruction of the scene’s color could be essential for many tasks in computer vision and DVS. We present a novel method for reconstructing a full spatial resolution, colored image utilizing the DVS and an active colored light source. We analyze the DVS response and present two reconstruction algorithms: linear-based and convolutional-neural-network-based. Our two presented methods reconstruct the colored image with high quality, and they do not suffer from any spatial resolution degradation as other methods. In addition, we demonstrate the robustness of our algorithm to changes in environmental conditions, such as illumination and distance. Finally, compared with previous works, we show how we reach the state-of-the-art results. We share our code on GitHub.

Original languageEnglish
Article number8327
Issue number19
StatePublished - Oct 2023


  • active illumination
  • color reconstruction
  • computational photography
  • dynamic vision sensor


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