Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging
🤖 AI Summary
This work addresses the incompatibility between asynchronous event streams from event cameras and conventional computational imaging systems based on linear forward models. It proposes the first invertible framework that unifies event-aware sensing with linear modeling of dynamic optical systems. By leveraging a physics-driven mapping, the method converts asynchronous events into pixel-wise logarithmic intensity and its temporal derivative, which are then embedded within a dynamic linear system characterized by a time-varying point spread function. Direct inverse filtering is achieved via frequency-domain Wiener deconvolution. The approach demonstrates high-precision source localization and separation in both simulated and real-world imaging scenarios using a tunable-focus telescope observing star fields, thereby validating its effectiveness and practicality for computational neuromorphic imaging.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
event vision sensors
neuromorphic imaging
linear systems
computational imaging
nonlinear representation
Innovation
Methods, ideas, or system contributions that make the work stand out.
event-based vision
linear systems theory
computational imaging
dynamic point spread function
Wiener deconvolution
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