Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging
This work provides a practical bridge between event sensing and model-based computational imaging for dynamic optical systems, addressing a specific bottleneck in neuromorphic imaging.
The authors tackled the challenge of integrating nonlinear event vision sensors with linear computational imaging models by developing a physics-grounded pipeline that maps event streams to log-intensity and derivative estimates, enabling inverse filtering via Wiener deconvolution. They validated this approach in simulations and real data from a telescope, demonstrating source localization and separability.
Event vision sensors (neuromorphic cameras) output sparse, asynchronous ON/OFF events triggered by log-intensity threshold crossings, enabling microsecond-scale sensing with high dynamic range and low data bandwidth. As a nonlinear system, this event representation does not readily integrate with the linear forward models that underpin most computational imaging and optical system design. We present a physics-grounded processing pipeline that maps event streams to estimates of per-pixel log-intensity and intensity derivatives, and embeds these measurements in a dynamic linear systems model with a time-varying point spread function. This enables inverse filtering directly from event data, using frequency-domain Wiener deconvolution with a known (or parameterised) dynamic transfer function. We validate the approach in simulation for single and overlapping point sources under modulated defocus, and on real event data from a tunable-focus telescope imaging a star field, demonstrating source localisation and separability. The proposed framework provides a practical bridge between event sensing and model-based computational imaging for dynamic optical systems.