Modelling dynamic systems transfer functions from events in computational neuromorphic imaging

2026-09-24

AI Summary

This study addresses the failure of conventional point spread functions (PSFs) and the absence of stimulus-to-readout mappings for event cameras in static scenes, where no events are generated. We construct a joint analytical framework integrating linear shift-invariant optical systems with event pixels. By introducing step, linear, and exponential temporal probes, we derive dynamic transfer functions and establish a unified forward operator from scene to events. This work defines, for the first time, an event-based dynamic PSF and reveals analytical correspondences between temporal probes and event statistics. Our analysis demonstrates that, unlike step probes which are susceptible to threshold mismatch, linear and exponential probes remain robust under high-mismatch conditions, thereby validating the proposed theoretical framework.

Abstract

Event Vision Sensing (EVS) report threshold crossings of log-irradiance, so a static optical system imaging a static scene produces no output at all. The classical procedure for measuring a Point Spread Function (PSF), illuminating the system with a constant point source, therefore has no event-based equivalent: the probe must carry a temporal profile, and that profile becomes part of the measurement. A growing body of Computational Neuromorphic Imaging (CNI) work already exploits this, pairing engineered or modulated optics with event sensing, but each system adopts a particular excitation together with a particular reading of the event stream without the correspondence between the two being stated. We examine that correspondence directly within a analytical framework of an Linear Shift-Invariant (LSI) optical system with a specified Modulation Transfer Function (MTF), a first-order filter EVS pixel model, and three different temporal probes: a step function, a linear ramp and an exponential ramp. By analysing the inverse of the entire chain for different event-statistic, and comparing the results to the specified MTF, we identify the context where each probe is most relevant. We consider how photon-noise and cross-array threshold mismatch effects the analytical accuracy of the probe-inverse. Results show that the widely used step probe is highly susceptible to mismatch while resilient to photon shot-noise, while a linear rise probe and exponential rise probe retain their ability to infer signal levels even with high mismatch. We discuss the potential of dynamic-PSFs as components of a full forward operator from scene to events. In this, we use this analytical description to define dynamic-PSFs around EVS, and discuss the gaps toward a unified pixel model and a scene-composition framework required for CNI.

Problem

• Computational Neuromorphic Imaging
• Event Vision Sensing
• Point Spread Function
• Modulation Transfer Function
• Dynamic Systems Modelling

Innovation

• Computational Neuromorphic Imaging
• Event Vision Sensing
• Dynamic Point Spread Function
• Modulation Transfer Function
• Temporal Probes