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

arXiv: https://arxiv.org/abs/2603.14023 · HTML: https://mlsanity.com/paper/2603.14023/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 58 references that are in the map.
- First posted on arXiv 14 Mar 2026.

## Builds on (most cited first, 22 of 22)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [High Speed and High Dynamic Range Video with an Event Camera](https://mlsanity.com/paper/1906.07165/) (2019)
- [Learning Blind Video Temporal Consistency](https://mlsanity.com/paper/1808.00449/) (2018)
- [Spectral Norm Regularization for Improving the Generalizability of Deep Learning](https://mlsanity.com/paper/1705.10941/) (2017)
- [Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform](https://mlsanity.com/paper/2107.11627/) (2021)
- [HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks](https://mlsanity.com/paper/2305.06382/) (2023)
- [Imaging through the Atmosphere using Turbulence Mitigation Transformer](https://mlsanity.com/paper/2207.06465/) (2022)
- [Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence](https://mlsanity.com/paper/2009.00071/) (2020)
- [Spatio-Temporal Turbulence Mitigation: A Translational Perspective](https://mlsanity.com/paper/2401.04244/) (2024)
- [Microsaccade-inspired Event Camera for Robotics](https://mlsanity.com/paper/2405.17769/) (2024)
- [EventLFM: Event Camera integrated Fourier Light Field Microscopy for Ultrafast 3D imaging](https://mlsanity.com/paper/2310.00730/) (2023)
- [TurbuGAN: An Adversarial Learning Approach to Spatially-Varying Multiframe Blind Deconvolution with Applications to Imaging Through Turbulence](https://mlsanity.com/paper/2203.06764/) (2022)
- [Learning Phase Distortion with Selective State Space Models for Video Turbulence Mitigation](https://mlsanity.com/paper/2504.02697/) (2025)
- [EventMamba: Enhancing Spatio-Temporal Locality with State Space Models for Event-Based Video Reconstruction](https://mlsanity.com/paper/2503.19721/) (2025)
- [Event fields: Capturing light fields at high speed, resolution, and dynamic range](https://mlsanity.com/paper/2412.06191/) (2024)
- [Guidestar-free image-guided wavefront-shaping](https://mlsanity.com/paper/2007.03956/) (2020)
- [WaveMo: Learning Wavefront Modulations to See Through Scattering](https://mlsanity.com/paper/2404.07985/) (2024)
- [Wavefront Estimation From a Single Measurement: Uniqueness and Algorithms](https://mlsanity.com/paper/2504.09395/) (2025)
- [EvTurb: Event Camera Guided Turbulence Removal](https://mlsanity.com/paper/2508.10582/) (2025)
- [EGTM: Event-guided Efficient Turbulence Mitigation](https://mlsanity.com/paper/2509.03808/) (2025)
- [Guidestar-Free Adaptive Optics with Asymmetric Apertures](https://mlsanity.com/paper/2602.07029/) (2026)

## Abstract (arXiv)

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.