Practical Noise Modeling for SPAD Intensity Imaging

2026-08-01

AI Summary

This work addresses the challenge of accurately modeling and removing complex noise in intensity imaging with single-photon avalanche diode (SPAD) cameras, which is hindered by exposure-dependent dark counts, exposure-independent dark-frame bias, and pixel response non-uniformity. The study presents the first comprehensive forward noise model for timestamp-free SPAD intensity imaging, grounded in the binary frame accumulation process. It introduces a dedicated SPAD-DSC (Dark-frame Shadow Correction) method along with a tailored calibration pipeline. Leveraging this model, the authors construct a real-world SPAD intensity dataset and develop a count-domain noise synthesis strategy to enable training of deep denoising networks. Experimental results demonstrate that the proposed framework substantially improves denoising performance, confirming both the validity and practical utility of the model.

Abstract

Single-photon avalanche diode (SPAD) cameras are promising for low-light and high-dynamic-range intensity imaging, but their practical use is limited by complex sensor-specific noise. Unlike time-correlated single-photon counting (TCSPC) systems, SPAD cameras record whether at least one detection occurred in each gate without photon timestamps in intensity imaging mode, making explicit noise decomposition difficult. We present a practical noise modeling and calibration framework for SPAD intensity denoising. Our forward model describes binary-frame accumulation with a Binomial observation process, models signal-independent dark noise as an exposure-dependent pure dark count term plus an exposure-independent dark-frame bias term, and incorporates pixel-wise response non-uniformity. We design a dedicated calibration procedure for the proposed model and use it to build a count-domain noise-synthesis pipeline for network training. For denoising, we further design a SPAD-specific dark-shading correction (SPAD-DSC) to remove most systematic noise before network training. We construct a real-world SPAD intensity dataset for testing. Experimental results demonstrate the superiority of the proposed noise model.

Problem

• SPAD
• intensity imaging
• noise modeling
• dark noise
• sensor-specific noise

Innovation

• SPAD intensity imaging
• noise modeling
• dark noise decomposition
• pixel-wise non-uniformity
• dark-shading correction