# Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing

Source: [Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing](<https://www.sota2.com/research/paper/68fa7ae21e209131cea85a75>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:29:46.045Z

Authors: Tristan S.W. Stevens, Ois\\'in Nolan, Ruud J.G. van Sloun

Published: 2025-08-24T12:20:18.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2508.17326>)

[PDF](<https://arxiv.org/pdf/2508.17326.pdf>)