发表机构
Carnegie Mellon University; University of Wisconsin-Madison（卡内基梅隆大学; 威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对单光子相机产生的二值图像逆问题，提出基于超几何帧缩减过程的生成框架，刻画光子预算增加时场景理解从随机到确定的转变，并评估其对字符识别、二维码解码和面部分析等下游任务的影响。
AI 中文摘要
单光子雪崩二极管（SPAD）相机由于其光子计数的特性，其工作方式与传统相机根本不同。每一帧都会产生一幅二值图像：如果在曝光期间没有光子到达，像素报告为0；如果有一个或多个光子到达，像素报告为1。从单个二值帧重建场景或推断其属性是困难的，因为许多不同的图像可能产生相同的测量结果；因此，逆问题从根本上是一对多的。随着我们收集更多的二值测量，与逆问题及其相关推断相关的固有不确定性会减小。当光子计数足够时，相对于信号均值，光子噪声变得可忽略不计，从而实现近乎确定性的场景恢复和推断。这项工作刻画了随着光子预算的增加，场景理解从随机到近乎确定性的转变，分析了光子到达的随机性如何影响下游推断任务。在技术上，我们开发了一个基于超几何帧缩减过程的条件生成框架，用于累积的二值SPAD测量。生成模型捕获了光子匮乏逆问题的一对多特性，从而能够经验性地刻画这种模糊性如何随着测量次数的增加而减小，以及其对字符识别、二维码解码和面部分析等下游任务的影响。
英文摘要
Single-photon avalanche diode (SPAD) cameras operate fundamentally differently from conventional cameras due to their photon-counting nature. Each frame produces a binary image: pixels report zero if no photons arrived during exposure, and one if one or more photons arrived. Reconstructing a scene or inferring its properties from a single binary frame is difficult because many different images could produce the same measurement; thus, the inverse problem is fundamentally one-to-many. As we gather more binary measurements, the inherent uncertainty associated with the inverse problem and any associated inference diminishes. With sufficient photon counts, photon noise becomes negligible relative to the signal mean, enabling near-deterministic scene recovery and inference. This work characterizes the transition from stochastic to near-deterministic scene understanding as photon budget increases, analyzing how the stochasticity in photon arrival affects downstream inference tasks. Technically, we develop a conditional generative framework based on a Hypergeometric frame-thinning process for accumulated binary SPAD measurements. Generative models capture the one-to-many nature of photon-starved inverse problems, enabling empirical characterization of how this ambiguity diminishes with increasing measurements and its impact on downstream tasks like character recognition, QR code decoding, and facial analysis.
Comments18 pages, 18 figures
