Accurate Simulation Pipeline for Passive Single-Photon Imaging

• URL: http://arxiv.org/abs/2601.12850v1
• Date: Mon, 19 Jan 2026 09:04:43 GMT
• Title: Accurate Simulation Pipeline for Passive Single-Photon Imaging
• Authors: Aleksi Suonsivu, Lauri Salmela, Leevi Uosukainen, Edoardo Peretti, Radu Ciprian Bilcu, Giacomo Boracchi,
• Abstract summary: Single-Photon Avalanche Diodes (SPADs) are new and promising imaging sensors.<n>Due to the high price and limited availability of SPAD sensors, the demand for an accurate data simulation pipeline is substantial.<n>We present a comprehensive SPAD simulation pipeline and validate it with multiple experiments using two recent commercial SPAD sensors.
• Score: 13.13653190702911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Towards Realistic Low-Light Image Enhancement via ISP Driven Data Modeling [61.95831392879045]
  Deep neural networks (DNNs) have recently become the leading method for low-light image enhancement (LLIE)<n>Despite significant progress, their outputs may still exhibit issues such as amplified noise, incorrect white balance, or unnatural enhancements when deployed in real world applications.<n>A key challenge is the lack of diverse, large scale training data that captures the complexities of low-light conditions and imaging pipelines.<n>We propose a novel image signal processing (ISP) driven data synthesis pipeline that addresses these challenges by generating unlimited paired training data.
  arXiv  Detail & Related papers  (2025-04-16T15:53:53Z)
• A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data [5.378429123269604]
  Single-photon avalanche diodes (SPADs) are advanced sensors capable of detecting individual photons and recording their arrival times with picosecond resolution.<n>We propose a novel computational imaging algorithm to improve the 3D reconstruction of moving scenes from SPAD data.
  arXiv  Detail & Related papers  (2024-12-12T16:33:06Z)
• FoveaSPAD: Exploiting Depth Priors for Adaptive and Efficient Single-Photon 3D Imaging [7.350208716861244]
  Single-photon avalanche diodes (SPADs) are an emerging image-sensing technology that offer many advantages such as extreme sensitivity and time resolution.<n>In this paper, we propose new algorithms and sensing policies that improve signal-to-noise ratio (SNR) and increase computing and memory efficiency.
  arXiv  Detail & Related papers  (2024-12-03T00:20:01Z)
• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• Adaptive Domain Learning for Cross-domain Image Denoising [57.4030317607274]
  We present a novel adaptive domain learning scheme for cross-domain image denoising. We use existing data from different sensors (source domain) plus a small amount of data from the new sensor (target domain) The ADL training scheme automatically removes the data in the source domain that are harmful to fine-tuning a model for the target domain. Also, we introduce a modulation module to adopt sensor-specific information (sensor type and ISO) to understand input data for image denoising.
  arXiv  Detail & Related papers  (2024-11-03T08:08:26Z)
• bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction [57.199618102578576]
  We propose bit2bit, a new method for reconstructing high-quality image stacks at original resolution from sparse binary quantatemporal image data. Inspired by recent work on Poisson denoising, we developed an algorithm that creates a dense image sequence from sparse binary photon data. We present a novel dataset containing a wide range of real SPAD high-speed videos under various challenging imaging conditions.
  arXiv  Detail & Related papers  (2024-10-30T17:30:35Z)
• Time-Resolved MNIST Dataset for Single-Photon Recognition [4.019891355693911]
  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.
  arXiv  Detail & Related papers  (2024-10-22T06:58:37Z)
• Photon Inhibition for Energy-Efficient Single-Photon Imaging [19.816230454712585]
  Single-photon cameras (SPCs) are emerging as sensors of choice for challenging imaging applications. Yet, single-photon sensitivity in SPADs comes at a cost -- each photon detection consumes more energy than that of a CMOS camera. We propose a computational-imaging approach called emphphoton inhibition to address this challenge.
  arXiv  Detail & Related papers  (2024-09-26T23:19:44Z)
• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• Diffusion Models for Interferometric Satellite Aperture Radar [73.01013149014865]
  Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue.
  arXiv  Detail & Related papers  (2023-08-31T16:26:17Z)
• Simulating single-photon detector array sensors for depth imaging [2.497104612216142]
  Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. We establish a robust yet simple numerical procedure that establishes the fundamental limits to depth imaging with SPAD arrays.
  arXiv  Detail & Related papers  (2022-10-07T13:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.