High-speed Imaging through Turbulence with Event-based Light Fields
This addresses the challenge of high-speed imaging in turbulent environments for applications like remote sensing or surveillance, representing a novel integration rather than an incremental improvement.
The paper tackles the problem of imaging fast-moving non-rigid objects through strong atmospheric turbulence by introducing the first system using event-based light field cameras, achieving high frame rates and disambiguating motion from turbulence to image objects traveling up to 16,000 pixels per second.
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.