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：商用微波链路（CMLs）在降雨感知中提供了高密度的空间覆盖，但其路径积分测量方式使得精确的地面场重建面临挑战。现有方法通常将CMLs简单化为点传感器，忽略降雨与信号衰减之间的线积分关系，导致在非均匀降水条件下性能退化。本研究将雨量场重建视为以扩散模型（DMs）作为高保真空间先验的贝叶斯逆问题。研究表明，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。将降雨估计建模为具有DM先验的贝叶斯逆问题，可支持使用Plug-and-Play、序贯蒙特卡洛及副本交换等广泛方法族实现免训练后验采样。在合成与真实数据集上的实验表明，该方法相较于基于CMLs的现有重建基准具有持续改进效果。