Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
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.
68ae137a364411bea07df678
Tristan S. W. Stevens
68ae137a364411bea07df679
Oisín Nolan
68ae137a364411bea07df67a
Ruud J. G. van Sloun
Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing
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.
A semantic-guided diffusion-based dehazing algorithm improves echocardiography image quality by integrating a pixel-wise noise model and a generative prior.
semantic-guided diffusion-based dehazing pixel-wise noise model diffusion posterior sampling generative prior echocardiography MICCAI Dehazing Echocardiography Challenge DehazingEcho2025