生成首个用于单光子成像的时序化MNIST数据集，推动低光条件下的深度学习研究。
Time-Resolved MNIST Dataset for Single-Photon Recognition
- 基于物理过程模拟光子到达与噪声，生成真实时间戳光子流
- 构建可扩展的时序化SPAD阵列数据集，支持任意尺寸图像生成
- 适合研究低光成像、神经网络在极低光照下的性能
时间分辨单光子成像是一种能记录单个光子到达时间的成像技术，具有独特优势。目前主流采用单光子雪崩二极管（SPAD）实现，适用于异步读出的被动成像，但受限于小规模阵列，缺乏可用于被动模式的时间分辨SPAD成像数据集，阻碍了相关研究进展。本文提出一种真实的SPAD成像仿真流程，综合考虑光子到达的随机性及采集过程中的各类噪声源。我们开发了软件原型，可生成任意尺寸的时间分辨SPAD阵列被动模式数据。从参考图像出发，生成包含时间戳的光子检测序列。基于此，我们构建了时间分辨版MNIST数据集并公开发布，旨在推动时间分辨SPAD成像的新研究方向，并探索卷积神经网络（CNN）在极端低光条件下的表现。
原文摘要 · Abstract (English)
Time-resolved single photon imaging is a promising imaging modality characterized by the unique capability of timestamping the arrivals of single photons. Single-Photon Avalanche Diodes (SPADs) are the leading technology for implementing modern time-resolved pixels, suitable for passive imaging with asynchronous readout. However, they are currently limited to small sized arrays, thus there is a lack of datasets for passive time-resolved SPAD imaging, which in turn hinders research on this peculiar imaging data. In this paper we describe a realistic simulation process for SPAD imaging, which takes into account both the stochastic nature of photon arrivals and all the noise sources involved in the acquisition process of time-resolved SPAD arrays. We have implemented this simulator in a software prototype able to generate arbitrary-sized time-resolved SPAD arrays operating in passive mode. Starting from a reference image, our simulator generates a realistic stream of timestamped photon detections. We use our simulator to generate a time-resolved version of MNIST, which we make publicly available. Our dataset has the purpose of encouraging novel research directions in time-resolved SPAD imaging, as well as investigating the performance of CNN classifiers in extremely low-light conditions.
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