Single-Photon Avalanche Diodes (SPADs) are new and promising imaging sensors. These sensors are sensitive enough to detect individual photons hitting each pixel, with extreme temporal resolution and without readout noise. Thus, SPADs stand out as an optimal choice for low-light imaging. Due to the high price and limited availability of SPAD sensors, the demand for an accurate data simulation pipeline is substantial. Indeed, the scarcity of SPAD datasets hinders the development of SPAD-specific processing algorithms and impedes the training of learning-based solutions. In this paper, we present a comprehensive SPAD simulation pipeline and validate it with multiple experiments using two recent commercial SPAD sensors. Our simulator is used to generate the SPAD-MNIST, a single-photon version of the seminal MNIST dataset, to investigate the effectiveness of convolutional neural network (CNN) classifiers on reconstructed fluxes, even at extremely low light conditions, e.g., 5 mlux. We also assess the performance of classifiers exclusively trained on simulated data on real images acquired from SPAD sensors at different light conditions. The synthetic dataset encompasses different SPAD imaging modalities and is made available for download. Project page: https://boracchi.faculty.polimi.it/Projects/SPAD-MNIST.html.

翻译：单光子雪崩二极管（SPAD）是一种新型且前景广阔的成像传感器。这类传感器灵敏度极高，能够检测到每个像素上撞击的单个光子，同时具备极佳的时间分辨率且无读出噪声。因此，SPAD成为弱光成像的理想选择。由于SPAD传感器价格昂贵且获取受限，对精确数据仿真流程的需求十分迫切。事实上，SPAD数据集的稀缺阻碍了针对SPAD的专用处理算法的发展，也限制了基于学习的解决方案的训练。本文提出了一套全面的SPAD仿真流程，并利用两款最新的商用SPAD传感器通过多项实验对其进行了验证。我们的仿真器用于生成SPAD-MNIST——经典MNIST数据集的单光子版本，以研究卷积神经网络（CNN）分类器在重建光通量上的有效性，即使在极低光照条件下（例如5毫勒克斯）亦然。我们还评估了仅使用仿真数据训练的分类器在不同光照条件下从SPAD传感器获取的真实图像上的性能。该合成数据集涵盖了不同的SPAD成像模式，并已开放下载。项目页面：https://boracchi.faculty.polimi.it/Projects/SPAD-MNIST.html。