将事件传感器数据转化为线性模型，实现动态光学系统的直接逆滤波
Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging
- 构建物理基础的处理流程，将事件流映射为像素级对数强度与梯度
- 在时变点扩散函数下实现频域维纳反卷积，完成事件数据直接逆滤波
- 适用于动态光学系统中的光源定位与分离，适合实时成像研究者
事件视觉传感器（类脑相机）以稀疏、异步的ON/OFF事件形式输出，由对数强度阈值触发，实现微秒级感知，具有高动态范围和低带宽特点。由于其非线性特性，难以与多数计算成像和光学系统设计所依赖的线性前向模型兼容。本文提出一个基于物理原理的处理流程，将事件流映射为每个像素的对数强度和强度导数估计，并嵌入具有时变点扩散函数的动态线性系统模型中。由此可直接从事件数据进行逆滤波，采用频域维纳反卷积，结合已知或参数化动态传递函数。在模拟中验证了单点源与重叠点源在调制离焦条件下的有效性，并在真实事件数据上使用可调焦望远镜观测星场，成功实现光源定位与可分性。该框架为事件感知与基于模型的动态光学系统计算成像提供了实用桥梁。
原文摘要 · Abstract (English)
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.
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