# A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images

arXiv: https://arxiv.org/abs/2101.09122 · HTML: https://mlsanity.com/paper/2101.09122/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 84 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising](https://mlsanity.com/paper/1807.04364/) (2018)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound](https://mlsanity.com/paper/1601.07014/) (2016)
- [Block-Matching Convolutional Neural Network for Image Denoising](https://mlsanity.com/paper/1704.00524/) (2017)

## Built on by (most cited first, 5 of 5)

- [Optimal Density Functions for Weighted Convolution in Learning Models](https://mlsanity.com/paper/2505.24527/) (2025)
- [Optimal Weighted Convolution for Classification and Denosing](https://mlsanity.com/paper/2505.24558/) (2025)
- [Learning-Based and Quality Preserving Super-Resolution of Noisy Images](https://mlsanity.com/paper/2311.02254/) (2023)
- [Framework of a multiscale data-driven DT of the musculoskeletal system](https://mlsanity.com/paper/2506.11821/) (2025)
- [Pyramid Self-Contrastive Learning for Single-shot Test-time Ultrasound Image Denoising](https://mlsanity.com/paper/2605.12567/) (2026)

## Abstract (arXiv)

Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g., segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g., spectral, low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.