# Illumination-Based Color Reconstruction for the Dynamic Vision Sensor

arXiv: https://arxiv.org/abs/2211.06695 · HTML: https://mlsanity.com/paper/2211.06695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Events-to-Video: Bringing Modern Computer Vision to Event Cameras](https://mlsanity.com/paper/1904.08298/) (2019)
- [Event-based Stereo Visual Odometry](https://mlsanity.com/paper/2007.15548/) (2020)
- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://mlsanity.com/paper/1607.06283/) (2016)
- [Event-Based Angular Velocity Regression with Spiking Networks](https://mlsanity.com/paper/2003.02790/) (2020)
- [CED: Color Event Camera Dataset](https://mlsanity.com/paper/1904.10772/) (2019)
- [A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging](https://mlsanity.com/paper/2106.15944/) (2021)

## Built on by (most cited first, 1 of 1)

- [Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision](https://mlsanity.com/paper/2602.23514/) (2026)

## Abstract (arXiv)

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. We present a novel method to reconstruct a full spatial resolution colored image with 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. In addition, we demonstrate our algorithm robustness to changes in environmental conditions such as illumination and distance. Finally, comparing with previous works, we show how we reach the state of the art results.