AI 中文总结
针对SPAD强度成像的噪声分解难题，提出含二项式观测过程等的噪声建模与校准框架，设计SPAD-DSC校正系统噪声，构建测试数据集并验证模型优越性。
AI 中文摘要
单光子雪崩二极管（SPAD）相机在低光和高动态范围强度成像领域具有应用前景，但其实际应用受限于传感器特有的复杂噪声。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记录每个门控周期内是否至少发生一次探测，不记录光子时间戳，这使得显式噪声分解变得困难。我们提出一种用于SPAD强度去噪的实用噪声建模与校准框架。该正向模型用二项式观测过程描述二进制帧累积，将与信号无关的暗噪声建模为与曝光相关的纯暗计数项加上与曝光无关的暗帧偏置项，并纳入像素级响应非均匀性。我们为所提模型设计专用校准流程，并用其构建用于网络训练的计数域噪声合成流水线。对于去噪，我们进一步设计SPAD专用暗阴影校正（SPAD-DSC），在网络训练前去除大部分系统噪声。我们构建真实世界SPAD强度数据集用于测试，实验结果证明了所提噪声模型的优越性。
英文摘要
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.
