Gradient events: improved acquisition of visual information in event cameras

TL;DR

This paper introduces gradient events for event cameras, significantly enhancing visual information acquisition by reducing sensitivity to oscillating light and improving grayscale video reconstruction.

Contribution

It proposes a new gradient event type that improves video reconstruction and robustness to flickering light, outperforming existing brightness event methods.

Findings

Gradient events enable better grayscale video reconstruction.

Gradient event-based methods outperform state-of-the-art brightness event methods.

The approach reduces sensitivity to oscillating light sources.

Abstract

The current event cameras are bio-inspired sensors that respond to brightness changes in the scene asynchronously and independently for every pixel, and transmit these changes as ternary event streams. Event cameras have several benefits over conventional digital cameras, such as significantly higher temporal resolution and pixel bandwidth resulting in reduced motion blur, and very high dynamic range. However, they also introduce challenges such as the difficulty of applying existing computer vision algorithms to the output event streams, and the flood of uninformative events in the presence of oscillating light sources. Here we propose a new type of event, the gradient event, which benefits from the same properties as a conventional brightness event, but which is by design much less sensitive to oscillating light sources, and which enables considerably better grayscale frame reconstruction. We show that the gradient event -based video reconstruction outperforms existing state-of-the-art brightness event -based methods by a significant margin, when evaluated on publicly available event-to-video datasets. Our results show how gradient information can be used to significantly improve the acquisition of visual information by an event camera.