基于事件传感的线性系统框架用于计算神经形态成像
Optical Linear Systems Framework for Event Sensing and Computational Neuromorphic Imaging
AI总结：
本文提出了一种基于事件传感的线性系统框架，用于动态光学系统的计算神经形态成像，通过物理建模实现事件数据的逆滤波与源定位。
摘要：
事件视觉传感器（神经形态相机）输出稀疏的异步ON/OFF事件，这些事件由对数强度阈值跨越触发，使在微秒级尺度上实现高动态范围和低数据带宽的传感。作为一种非线性系统，这种事件表示不容易与大多数计算成像和光学系统设计所依赖的线性前向模型集成。我们提出了一种基于物理的处理流程，将事件流映射到每个像素的对数强度和强度导数估计，并将这些测量嵌入具有时间变化点扩散函数的动态线性系统模型中。这使事件数据可以直接用于逆滤波，使用频域维纳反卷积与已知（或参数化）的动态传递函数。我们在模拟中验证了该方法在单个和重叠点源在调制模糊情况下的有效性，并在实际事件数据上测试了可调焦望远镜成像恒星场，展示了源定位和分离能力。所提出的框架为动态光学系统中的事件传感和基于模型的计算成像提供了实用的桥梁。
英文摘要：
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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