为SPAD相机设计实用噪声模型，提升低光成像质量
Practical Noise Modeling for SPAD Intensity Imaging
- 用二项分布建模帧累积，分离暗噪声与响应不均
- 实测数据验证模型优于传统方法，显著降低系统噪声
- 适合从事低光成像、传感器校准的科研与工程人员
单光子雪崩二极管（SPAD）相机在低光和高动态范围强度成像中具有潜力，但其实际应用受限于复杂的传感器特异性噪声。与时间相关单光子计数（TCSPC）系统不同，SPAD相机在强度成像模式下仅记录每个门控期内是否有至少一次探测事件，而不提供光子时间戳，导致显式噪声分解困难。本文提出一种面向SPAD强度去噪的实用噪声建模与校准框架。前向模型采用二项分布描述二值帧累积，将信号无关的暗噪声建模为随曝光变化的纯暗计数项与随曝光不变的暗帧偏置项，并引入像素级响应非均匀性。设计专用校准流程构建该模型，并用于生成计数域噪声合成数据以训练网络。针对去噪，进一步设计了SPAD特定的暗照度校正（SPAD-DSC），在训练前消除大部分系统噪声。构建真实世界SPAD强度数据集用于测试。实验结果表明所提噪声模型具有优越性能。
原文摘要 · Abstract (English)
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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