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arXiv · 2608.00489

Practical Noise Modeling for SPAD Intensity Imaging

Wendi Liu·Yujie Lu·Zengxi Zhang·Haiyang Jiang·Weihang Ran·Yinqiang Zheng
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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.

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Wendi Liu, Yujie Lu, Zengxi Zhang, Haiyang Jiang, Weihang Ran, Yinqiang Zheng. 2026-08-01. Practical Noise Modeling for SPAD Intensity Imaging. https://arxiv.org/abs/2608.00489

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