High-speed Imaging through Turbulence with Event-based Light Fields

TL;DR

This paper presents a novel event-based light field imaging system that captures high-speed, non-rigid objects through atmospheric turbulence, leveraging multiple views and machine learning to distinguish scene motion from turbulence effects.

Contribution

The work introduces the first system combining event-based light fields with machine learning to effectively image fast-moving objects through turbulence.

Findings

Successfully imaging objects moving at up to 16,000 pixels/sec

Disambiguating scene motion from turbulence using multi-view correlations

Achieving high frame rate imaging through atmospheric turbulence

Abstract

This work introduces and demonstrates the first system capable of imaging fast-moving extended non-rigid objects through strong atmospheric turbulence at high frame rate. Event cameras are a novel sensing architecture capable of estimating high-speed imagery at thousands of frames per second. However, on their own event cameras are unable to disambiguate scene motion from turbulence. In this work, we overcome this limitation using event-based light field cameras: By simultaneously capturing multiple views of a scene, event-based light field cameras and machine learning-based reconstruction algorithms are able to disambiguate motion-induced dynamics, which produce events that are strongly correlated across views, from turbulence-induced dynamics, which produce events that are weakly correlated across view. Tabletop experiments demonstrate event-based light field can overcome strong turbulence while imaging high-speed objects traveling at up to 16,000 pixels per second.