Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911386972454912 |
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| author | Kruger, Nimrod Ralph, Nicholas Owen Cohen, Gregory Hurley, Paul |
| author_facet | Kruger, Nimrod Ralph, Nicholas Owen Cohen, Gregory Hurley, Paul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13498 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging Kruger, Nimrod Ralph, Nicholas Owen Cohen, Gregory Hurley, Paul Computer Vision and Pattern Recognition 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. |
| title | Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.13498 |