Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging

• URL: http://arxiv.org/abs/2601.13498v1
• Date: Tue, 20 Jan 2026 01:23:22 GMT
• Title: Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging
• Abstract summary: Event vision sensors (neuromorphic cameras) output sparse, asynchronous ON/OFF events triggered by log-intensity threshold crossings.<n>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.<n>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.
• Score: 2.45990890510584
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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