Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis
It enables robust motion tracking in extreme low-light and high-speed scenarios, addressing a critical bottleneck for applications like biomedical imaging and autonomous navigation.
This work introduces a physics-informed neuromorphic framework that combines event sensing with speckle analysis to track fast-moving objects through scattering media, achieving 10x faster motion and 10x dimmer illumination compared to conventional systems.
This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from existing approaches that use frame-based cameras with fixed exposure times, which trade off signal-to-noise ratio for temporal resolution, we introduce computational neuromorphic tracking (CNT), a physics-informed framework that combines asynchronous event sensing with task-driven speckle analysis for robust motion estimation. We formulate the neuromorphic speckle aggregation as a spatiotemporal speckle representation, jointly optimizing the temporal and spatial parameters to maximize tracking stability under extreme conditions. Extensive experiments demonstrate that our method enables robust motion tracking of 10x faster motion and under 10x dimmer illumination compared to conventional systems. These improvements significantly broaden the operational regime for tracking through scattering media, providing an efficient and scalable solution for demanding scenarios involving rapid motion and low-light conditions.