# Deep Ultrasound Denoising Using Diffusion Probabilistic Models

arXiv: https://arxiv.org/abs/2306.07440 · HTML: https://mlsanity.com/paper/2306.07440/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 5 of 5)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [Deep Ultrasound Denoising Without Clean Data](https://mlsanity.com/paper/2201.02604/) (2022)
- [Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized Solutions](https://mlsanity.com/paper/2206.07926/) (2022)

## Built on by (most cited first, 12 of 12)

- [Dehazing Ultrasound using Diffusion Models](https://mlsanity.com/paper/2307.11204/) (2023)
- [Replication in Visual Diffusion Models: A Survey and Outlook](https://mlsanity.com/paper/2408.00001/) (2024)
- [Ultrasound Image Generation using Latent Diffusion Models](https://mlsanity.com/paper/2502.08580/) (2025)
- [Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2408.10987/) (2024)
- [Active inference and deep generative modeling for cognitive ultrasound](https://mlsanity.com/paper/2410.13310/) (2024)
- [Ultrasound Imaging based on the Variance of a Diffusion Restoration Model](https://mlsanity.com/paper/2403.15316/) (2024)
- [Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty](https://mlsanity.com/paper/2310.20618/) (2023)
- [Ultrasound Image Enhancement with the Variance of Diffusion Models](https://mlsanity.com/paper/2409.11380/) (2024)
- [Denoising diffusion models for high-resolution microscopy image restoration](https://mlsanity.com/paper/2409.12078/) (2024)
- [IRSDE-Despeckle: A Physics-Grounded Diffusion Model for Generalizable Ultrasound Despeckling](https://mlsanity.com/paper/2602.22717/) (2026)
- [Noise-Aware Boundary-Enhanced Generative Learning for Ultrasound Speckle Reduction](https://mlsanity.com/paper/2606.25009/) (2026)
- [Null-Space Diffusion Restoration with Adaptive Uncertainty-Guided Fusion for Ultrasound Speckle Reduction](https://mlsanity.com/paper/2608.29820/) (2026)

## Abstract (arXiv)

Ultrasound images are widespread in medical diagnosis for musculoskeletal, cardiac, and obstetrical imaging due to the efficiency and non-invasiveness of the acquisition methodology. However, the acquired images are degraded by acoustic (e.g. reverberation and clutter) and electronic sources of noise. To improve the Peak Signal to Noise Ratio (PSNR) of the images, previous denoising methods often remove the speckles, which could be informative for radiologists and also for quantitative ultrasound. Herein, a method based on the recent Denoising Diffusion Probabilistic Models (DDPM) is proposed. It iteratively enhances the image quality by eliminating the noise while preserving the speckle texture. It is worth noting that the proposed method is trained in a completely unsupervised manner, and no annotated data is required. The experimental blind test results show that our method outperforms the previous nonlocal means denoising methods in terms of PSNR and Generalized Contrast to Noise Ratio (GCNR) while preserving speckles.