Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
Authors: name: Tristan S. W. Stevens, name: Oisín Nolan, name: Ruud J. G. van Sloun

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.