Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate ground-level reconstruction challenging. Existing methods typically oversimplify CMLs as point sensors and neglect line integration relating rainfall to signal attenuation, resulting in degraded performance under heterogeneous precipitation. In this work, we view rain field reconstruction as a Bayesian inverse problem with Diffusion Models (DMs) as high-fidelity spatial priors. We show that diffusion models better preserve key rainfall statistics compared to censored Gaussian processes. Framing rainfall estimation as a Bayesian inverse problem with a DM prior enables training-free posterior sampling using a broad family of methods, including Plug-and-Play, Sequential Monte Carlo, and Replica Exchange methods. Experiments on synthetic and real-world datasets demonstrate consistent improvements over established CML-based reconstruction baselines.

翻译：商业微波链路（CMLs）具有密集空间覆盖能力，可用于降雨感知，但其生成的是路径积分测量值，这使得精确的地面重建面临挑战。现有方法通常将CMLs过度简化为点传感器，并忽略了将降雨与信号衰减联系起来的线积分，导致其在非均匀降水条件下性能下降。在本工作中，我们将雨场重建视为一个贝叶斯逆问题，并以扩散模型（DMs）作为高保真空间先验。我们证明，与截断高斯过程相比，扩散模型能更好地保留关键降雨统计特征。通过将降雨估计构建为具有DM先验的贝叶斯逆问题，即可利用Plug-and-Play、序贯蒙特卡洛和副本交换方法等一系列方法实现免训练后验采样。在合成数据集和真实数据集上的实验表明，该方法相较于现有基于CML的重建基线方法具有一致性的性能提升。