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.

翻译：事件视觉传感器（神经形态相机）输出由对数强度阈值交叉触发的稀疏、异步 ON/OFF 事件，实现了具有高动态范围和低数据带宽的微秒级传感。作为一个非线性系统，这种事件表示不易与支撑大多数计算成像和光学系统设计的线性前向模型相集成。我们提出了一个基于物理的处理流程，将事件流映射到像素级对数强度及强度导数的估计值，并将这些测量值嵌入到一个具有时变点扩散函数的动态线性系统模型中。这使得能够直接从事件数据进行逆滤波，使用具有已知（或参数化）动态传递函数的频域维纳反卷积。我们在模拟中验证了该方法在调制离焦下的单点源和重叠点源上的有效性，并在一个可调焦望远镜对星场成像的真实事件数据上进行了验证，展示了源定位和可分离性。所提出的框架为动态光学系统的事件传感与基于模型的计算成像之间提供了一个实用的桥梁。