Illumination-Based Color Reconstruction for the Dynamic Vision Sensor

TL;DR

This paper introduces a novel method for reconstructing colored images from the Dynamic Vision Sensor (DVS) using an active colored light source, employing linear and neural network algorithms to achieve state-of-the-art results.

Contribution

The paper presents a new approach combining DVS with active lighting and neural networks for color reconstruction, outperforming previous methods.

Findings

Achieved state-of-the-art color reconstruction accuracy.

Demonstrated robustness to environmental changes.

Compared favorably against previous techniques.

Abstract

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.