Illumination-Based Color Reconstruction for the Dynamic Vision Sensor
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
11 of 40 references.
References · 11
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
- 2019Event-based Vision: A Surveycited by 1,016
- 2018EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Camerascited by 224
- 2019Events-to-Video: Bringing Modern Computer Vision to Event Camerascited by 193
- 2020Event-based Stereo Visual Odometrycited by 105
- 2018Continuous-time Intensity Estimation Using Event Camerascited by 68
- 2016Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisationcited by 49
- 2020Event-Based Angular Velocity Regression with Spiking Networkscited by 41
- 2019CED: Color Event Camera Datasetcited by 37
- 2021A survey on computational spectral reconstruction methods from RGB to hyperspectral imagingcited by 20
Built on by
Citing papers per year: 2026: 1.
1 paper.