Noise2Params: Unification and Parameter Determination from Noise via a Probabilistic Event Camera Model

Noise2Params：通过概率事件相机模型实现噪声统一与参数确定

Abstract: Accurate, unified models for event cameras (ECs) remain elusive, hampering calibration and algorithm design. We develop a foundational probabilistic model for EC event detection, grounded in photon statistics, that unifies the description of static scene noise events and step response curves (S-curves) within a single analytical framework.

摘要： 事件相机（EC）的精确统一模型目前仍难以实现，这阻碍了校准和算法的设计。我们开发了一种基于光子统计的 EC 事件检测基础概率模型，该模型在单一分析框架内统一了静态场景噪声事件和阶跃响应曲线（S-曲线）的描述。

Three formulations of the probability distributions are derived, spanning all intensity regimes: exact Poisson, saddle-point, and Gaussian. The model reveals the underlying connection between these otherwise disparate EC behaviors and clarifies the interpretation of S-curves, which we show is more nuanced than selecting a fixed probability threshold.

我们推导了三种涵盖所有强度范围的概率分布公式：精确泊松分布、鞍点近似和高斯分布。该模型揭示了这些看似迥异的 EC 行为之间的内在联系，并阐明了 S-曲线的解释方式——我们证明其比单纯选择固定概率阈值要复杂得多。

Based on this model, we propose Noise2Params, a method for determining camera-specific values of the log-contrast threshold $B$, the lux-to-photon conversion factor $\alpha$, and the leakage term $\theta$ (found to be intensity dependent), via error minimization against observed noise-event distributions. Noise2Params requires only recordings of static, uniform scenes, offering an experimentally accessible alternative to approaches that demand specialized dynamic light sources.

基于此模型，我们提出了 Noise2Params，这是一种通过最小化观测噪声事件分布误差来确定相机特定参数的方法，包括对数对比度阈值 $B$、勒克斯到光子的转换因子 $\alpha$ 以及泄漏项 $\theta$（研究发现其与强度相关）。Noise2Params 仅需记录静态、均匀的场景，为那些需要专用动态光源的方法提供了一种更易于实验实现的替代方案。

We further support the validity the model by training convolutional neural networks (CNNs) on synthetic noise images generated from our distributions and evaluating their ability to reconstruct static scenes from experimental data. We further demonstrate the utility of our model by showing that CNNs incorporating synthetic data outperform those trained solely on experimental data. Our framework provides a quantitative foundation for EC calibration, noise-aware algorithm design, and applications in photon-limited regimes.

我们通过在由该分布生成的合成噪声图像上训练卷积神经网络（CNN），并评估其从实验数据中重建静态场景的能力，进一步验证了该模型的有效性。我们还通过展示结合合成数据的 CNN 优于仅使用实验数据训练的 CNN，证明了该模型的实用性。我们的框架为 EC 校准、噪声感知算法设计以及光子受限环境下的应用提供了定量基础。