# Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection

arXiv: https://arxiv.org/abs/2403.02750 · HTML: https://mlsanity.com/paper/2403.02750/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Boltzmann Machines and Denoising Autoencoders for Image Denoising](https://mlsanity.com/paper/1301.3468/) (2013)
- [Noise adaptive beamforming for linear array photoacoustic imaging](https://mlsanity.com/paper/2011.08414/) (2020)

## Built on by (most cited first, 1 of 1)

- [Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry](https://mlsanity.com/paper/2603.29289/) (2026)

## Abstract (arXiv)

Ultrasound is a widely used medical tool for non-invasive diagnosis, but its images often contain speckle noise which can lower their resolution and contrast-to-noise ratio. This can make it more difficult to extract, recognize, and analyze features in the images, as well as impair the accuracy of computer-assisted diagnostic techniques and the ability of doctors to interpret the images. Reducing speckle noise, therefore, is a crucial step in the preprocessing of ultrasound images. Researchers have proposed several speckle reduction methods, but no single method takes all relevant factors into account. In this paper, we compare seven such methods: Median, Gaussian, Bilateral, Average, Weiner, Anisotropic and Denoising auto-encoder without and with skip connections in terms of their ability to preserve features and edges while effectively reducing noise. In an experimental study, a convolutional noise-removing auto-encoder with skip connection, a deep learning method, was used to improve ultrasound images of breast cancer. This method involved adding speckle noise at various levels. The results of the deep learning method were compared to those of traditional image enhancement methods, and it was found that the proposed method was more effective. To assess the performance of these algorithms, we use three established evaluation metrics and present both filtered images and statistical data.