# Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging

arXiv: https://arxiv.org/abs/2601.13498 · HTML: https://mlsanity.com/paper/2601.13498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 20 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Continuous-time Intensity Estimation Using Event Cameras](https://mlsanity.com/paper/1811.00386/) (2018)
- [Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation](https://mlsanity.com/paper/1607.06283/) (2016)
- [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)
- [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)
- [Bilevel Inverse Problems in Neuromorphic Imaging](https://mlsanity.com/paper/2302.02052/) (2023)
- [Exploiting Alternating DVS Shot Noise Event Pair Statistics to Reduce Background Activity](https://mlsanity.com/paper/2304.03494/) (2023)
- [Seeing like a Cephalopod: Colour Vision with a Monochrome Event Camera](https://mlsanity.com/paper/2504.10984/) (2025)
- [Towards a physically realistic computationally efficient DVS pixel model](https://mlsanity.com/paper/2505.07386/) (2025)
- [Event2Audio: Event-Based Optical Vibration Sensing](https://mlsanity.com/paper/2507.03273/) (2025)

## Built on by (most cited first, 1 of 1)

- [Modelling dynamic systems transfer functions from events in computational neuromorphic imaging](https://mlsanity.com/paper/2609.29863/) (2026)

## Abstract (arXiv)

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.