标题： 基于语义扩散后验采样的心脏超声去雾 显示英文标题

标题： Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing

摘要： 超声心动图在心脏成像中起着核心作用，提供动态的心脏视图，这对于诊断和监测至关重要。 然而，由于多路径回波引起的雾气，图像质量可能会显著退化，尤其是在难以成像的患者中。 在这项工作中，我们提出了一种语义引导的、基于扩散的去雾算法，专为MICCAI去雾超声心动图挑战赛（DehazingEcho2025）而开发。 我们的方法结合了一个像素级的噪声模型，该模型从模糊输入的语义分割中得出，并整合到由生成先验引导的扩散后验采样框架中，该生成先验是在干净的超声数据上训练的。 在挑战数据集上的定量评估表明，在对比度和保真度指标上表现强劲。 提交算法的代码可在https://github.com/tristan-deep/semantic-diffusion-echo-dehazing获取。 显示英文摘要

摘要： Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded by haze arising from multipath reverberations, particularly in difficult-to-image patients. In this work, we propose a semantic-guided, diffusion-based dehazing algorithm developed for the MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method integrates a pixel-wise noise model, derived from semantic segmentation of hazy inputs into a diffusion posterior sampling framework guided by a generative prior trained on clean ultrasound data. Quantitative evaluation on the challenge dataset demonstrates strong performance across contrast and fidelity metrics. Code for the submitted algorithm is available at https://github.com/tristan-deep/semantic-diffusion-echo-dehazing.