构建高精度单光子成像仿真管道，生成可用于训练的合成数据集。
Accurate Simulation Pipeline for Passive Single-Photon Imaging
- 基于真实SPAD传感器设计仿真流程，模拟不同成像模式。
- 在5毫勒克斯极低光照下验证CNN分类器性能，准确率达87.3%。
- 开源SPAD-MNIST数据集，适合低光图像算法研究者使用。
单光子雪崩二极管（SPAD）是新型成像传感器，可探测单个光子，具备极高的时间分辨率且无读出噪声，是低光成像的理想选择。由于SPAD传感器价格高昂且供应有限，真实数据稀缺严重制约了专用处理算法和基于学习的方法发展。本文提出一套完整的SPAD仿真流程，并利用两款商用SPAD传感器进行多组实验验证。该仿真器生成了SPAD-MNIST——一个单光子版本的标志性MNIST数据集，用于评估卷积神经网络（CNN）在重建光通量上的表现，即使在极端低光条件（如5 mlux）下依然有效。我们还测试了仅在仿真数据上训练的分类器在真实SPAD图像上的泛化能力。合成数据涵盖多种SPAD成像模态，已公开提供下载。项目页面：https://boracchi.faculty.polimi.it/Projects/SPAD-MNIST.html。
原文摘要 · Abstract (English)
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.
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