# Modelling dynamic systems transfer functions from events in computational neuromorphic imaging

arXiv: https://arxiv.org/abs/2609.29863 · HTML: https://mlsanity.com/paper/2609.29863/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 17 of 17)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [Formulating Event-based Image Reconstruction as a Linear Inverse Problem with Deep Regularization using Optical Flow](https://mlsanity.com/paper/2112.06242/) (2021)
- [Event Camera Calibration of Per-pixel Biased Contrast Threshold](https://mlsanity.com/paper/2012.09378/) (2020)
- [Feedback control of event cameras](https://mlsanity.com/paper/2105.00409/) (2021)
- [EventLFM: Event Camera integrated Fourier Light Field Microscopy for Ultrafast 3D imaging](https://mlsanity.com/paper/2310.00730/) (2023)
- [Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patterns](https://mlsanity.com/paper/1811.10771/) (2018)
- [Time-Efficient Light-Field Acquisition Using Coded Aperture and Events](https://mlsanity.com/paper/2403.07244/) (2024)
- [CodedEvents: Optimal Point-Spread-Function Engineering for 3D-Tracking with Event Cameras](https://mlsanity.com/paper/2406.09409/) (2024)
- [TiDy-PSFs: Computational Imaging with Time-Averaged Dynamic Point-Spread-Functions](https://mlsanity.com/paper/2303.17583/) (2023)
- [Event Ellipsometer: Event-based Mueller-Matrix Video Imaging](https://mlsanity.com/paper/2411.17313/) (2024)
- [Event fields: Capturing light fields at high speed, resolution, and dynamic range](https://mlsanity.com/paper/2412.06191/) (2024)
- [SciDVS: A Scientific Event Camera with 1.7% Temporal Contrast Sensitivity at 0.7 lux](https://mlsanity.com/paper/2409.09648/) (2024)
- [Towards a physically realistic computationally efficient DVS pixel model](https://mlsanity.com/paper/2505.07386/) (2025)
- [Re-Interpreting the Step-Response Probability Curve to Extract Fundamental Physical Parameters of Event-based Vision Sensors](https://mlsanity.com/paper/2404.07656/) (2024)
- [Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging](https://mlsanity.com/paper/2601.13498/) (2026)
- [Coded-E2LF: Coded Aperture Light Field Imaging from Events](https://mlsanity.com/paper/2602.22620/) (2026)

## Abstract (arXiv)

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.