发表机构
Center for Mathematics and Artificial Intelligence and Department of Mathematical Sciences, George Mason University（数学与人工智能中心和数学科学系，乔治·马歇尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究单目基于事件的成像，将其公式化为合成孔径逆问题，通过聚合事件得到线性化模型，利用正则化反演恢复对数辐射率场，实验表明该方法能恢复大规模空间结构并抑制细粒度纹理。
AI 中文摘要
事件相机在对数辐射率变化超过固定对比度阈值时报告异步极性事件，产生有符号的时间对比度测量而非传统图像帧。我们将单目基于事件的成像公式化为静态地面域对数辐射率场\(\theta \in \mathbb{R}^{N_g}\)的合成孔径逆问题。通过聚合有限时间间隔内的事件得到线性化模型\(AP\theta = b+\eta\)，利用正则化反演恢复\(\theta\)。数值实验表明，相对于动态潜像和学习到的事件重建基线，基于\(\theta\)的公式能恢复连贯的大规模空间结构，同时抑制细粒度纹理。
英文摘要
Event cameras report asynchronous polarity events when changes in log--radiance exceed a fixed contrast threshold, producing signed temporal contrast measurements rather than conventional image frames. We formulate monocular event-based imaging as a synthetic-aperture inverse problem for a static ground-domain log--radiance field $θ\in \mathbb{R}^{N_g}$. Instead of reconstructing a latent pixel-time volume $v \in \mathbb{R}^{N_pN_t}$, we impose the geometric relation $v=Pθ$, where $P$ maps the fixed scene into motion-dependent latent views. Aggregating events over finite time intervals gives the linearized model \[ APθ= b+η, \] where $A$ is a temporal differencing operator, $b$ contains signed binned event counts, and $η$ represents measurement and modeling errors. This decomposition exposes a synthetic-aperture structure: under near-nadir motion, successive projections are approximately shifted views of a common scene, while the composite operator $AP$ remains ill-conditioned because it combines spatial averaging with temporal differencing. We therefore use regularized inversion to recover $θ$. Numerical experiments on simulated data and real near-nadir Falcon Neuro event data show that the proposed $θ$-based formulation recovers coherent large-scale spatial structure, relative to dynamic latent-image and learned event-reconstruction baselines, while suppressing fine-scale texture.
