Time-Resolved MNIST Dataset for Single-Photon Recognition

• URL: http://arxiv.org/abs/2410.16744v1
• Date: Tue, 22 Oct 2024 06:58:37 GMT
• Title: Time-Resolved MNIST Dataset for Single-Photon Recognition
• Authors: Aleksi Suonsivu, Lauri Salmela, Edoardo Peretti, Leevi Uosukainen, Radu Ciprian Bilcu, Giacomo Boracchi,
• Abstract summary: Time-resolved single photon imaging is a promising imaging modality characterized by the capability of timestamping the arrivals of single photons. SPADs are the leading technology for implementing modern time-resolved pixels, suitable for passive imaging with asynchronous readout. In this paper we describe a realistic simulation process for SPAD imaging, which takes into account both the unique nature of photon arrivals and all the noise sources involved in the acquisition process of time-resolved SPAD arrays.
• Score: 4.019891355693911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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