# Time-Resolved MNIST Dataset for Single-Photon Recognition

arXiv: https://arxiv.org/abs/2410.16744 · HTML: https://mlsanity.com/paper/2410.16744/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement](https://mlsanity.com/paper/1511.03995/) (2015)
- [The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM](https://mlsanity.com/paper/1610.08336/) (2016)
- [Getting to Know Low-light Images with The Exclusively Dark Dataset](https://mlsanity.com/paper/1805.11227/) (2018)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising](https://mlsanity.com/paper/2003.12751/) (2020)
- [Event-based Asynchronous Sparse Convolutional Networks](https://mlsanity.com/paper/2003.09148/) (2020)
- [Asynchronous Convolutional Networks for Object Detection in Neuromorphic Cameras](https://mlsanity.com/paper/1805.07931/) (2018)
- [Asynchronous Single-Photon 3D Imaging](https://mlsanity.com/paper/1908.06372/) (2019)
- [Learning from Event Cameras with Sparse Spiking Convolutional Neural Networks](https://mlsanity.com/paper/2104.12579/) (2021)
- [High Flux Passive Imaging with Single-Photon Sensors](https://mlsanity.com/paper/1902.10190/) (2019)
- [Passive Inter-Photon Imaging](https://mlsanity.com/paper/2104.00059/) (2021)
- [SoDaCam: Software-defined Cameras via Single-Photon Imaging](https://mlsanity.com/paper/2309.00066/) (2023)
- [Generalized Event Cameras](https://mlsanity.com/paper/2407.02683/) (2024)
- [Event-based Processing of Single Photon Avalanche Diode Sensors](https://mlsanity.com/paper/2001.02060/) (2019)
- [Coupling a Recurrent Neural Network to SPAD TCSPC Systems for Real-time Fluorescence Lifetime Imaging](https://mlsanity.com/paper/2306.15599/) (2023)

## Built on by (most cited first, 4 of 4)

- [Toward Robust Single-Photon Perception for Robots: A Condition-Aware Active Learning Approach](https://mlsanity.com/paper/2505.04376/) (2025)
- [Fundamental Recovery Bounds for SPAD Signals under Stationary Flux](https://mlsanity.com/paper/2601.07599/) (2026)
- [Accurate Simulation Pipeline for Passive Single-Photon Imaging](https://mlsanity.com/paper/2601.12850/) (2026)
- [SP-TransientBench: A Real-Captured Single Photon Perception Benchmark](https://mlsanity.com/paper/2606.18952/) (2026)

## Abstract (arXiv)

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.