发表机构
International Centre for Neuromorphic Systems; Western Sydney University（国际神经形态系统中心; 西悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在计算神经形态成像中，通过解析框架分析三种时间探针在LSI光学系统中的逆变换，评估其对MTF的推断能力，发现线性与指数探针在阈值失配下更稳健，并探讨动态PSF作为前向算子的潜力。
AI 中文摘要
事件视觉传感（EVS）报告对数辐照度的阈值交叉，因此对静态场景成像的静态光学系统完全不产生输出。测量点扩散函数（PSF）的经典方法是用恒定点光源照射系统，因此没有基于事件的等效方法：探针必须携带时间轮廓，而该轮廓成为测量的一部分。越来越多的计算神经形态成像（CNI）工作已经利用这一点，将工程化或调制的光学器件与事件传感配对，但每个系统采用特定的激励以及特定的事件流读取方式，而未说明两者之间的对应关系。我们在一个线性移不变（LSI）光学系统的解析框架内直接检查这种对应关系，该系统具有指定的调制传递函数（MTF）、一阶滤波器EVS像素模型，以及三种不同的时间探针：阶跃函数、线性斜坡和指数斜坡。通过分析整个链针对不同事件统计的逆，并将结果与指定的MTF进行比较，我们确定了每种探针最相关的背景。我们考虑了光子噪声和跨阵列阈值失配如何影响探针逆的解析精度。结果表明，广泛使用的阶跃探针极易受失配影响，但对光子散粒噪声具有弹性，而线性上升探针和指数上升探针即使在高失配情况下也能保持推断信号水平的能力。我们讨论了动态PSF作为从场景到事件的完整前向算子组件的潜力。在此，我们使用这种解析描述来定义围绕EVS的动态PSF，并讨论朝向统一像素模型和CNI所需的场景合成框架的差距。
英文摘要
Event Vision Sensing (EVS) report threshold crossings of log-irradiance, so a static optical system imaging a static scene produces no output at all. The classical procedure for measuring a Point Spread Function (PSF), illuminating the system with a constant point source, therefore has no event-based equivalent: the probe must carry a temporal profile, and that profile becomes part of the measurement. A growing body of Computational Neuromorphic Imaging (CNI) work already exploits this, pairing engineered or modulated optics with event sensing, but each system adopts a particular excitation together with a particular reading of the event stream without the correspondence between the two being stated. We examine that correspondence directly within a analytical framework of an Linear Shift-Invariant (LSI) optical system with a specified Modulation Transfer Function (MTF), a first-order filter EVS pixel model, and three different temporal probes: a step function, a linear ramp and an exponential ramp. By analysing the inverse of the entire chain for different event-statistic, and comparing the results to the specified MTF, we identify the context where each probe is most relevant. We consider how photon-noise and cross-array threshold mismatch effects the analytical accuracy of the probe-inverse. Results show that the widely used step probe is highly susceptible to mismatch while resilient to photon shot-noise, while a linear rise probe and exponential rise probe retain their ability to infer signal levels even with high mismatch. We discuss the potential of dynamic-PSFs as components of a full forward operator from scene to events. In this, we use this analytical description to define dynamic-PSFs around EVS, and discuss the gaps toward a unified pixel model and a scene-composition framework required for CNI.
CommentsConference paper - SPIE Sensors + Imaging 2026
