# Sparse Mixture-of-Experts for Non-Uniform Noise Reduction in MRI Images

arXiv: https://arxiv.org/abs/2501.14198 · HTML: https://mlsanity.com/paper/2501.14198/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts.
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Placed in

- Mixture-of-experts / [Medical and biomedical MoE](https://mlsanity.com/moe/medical-and-biomedical-moe/)

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [Adversarial Signal Denoising with Encoder-Decoder Networks](https://mlsanity.com/paper/1812.08555/) (2018)

## Built on by (most cited first, 3 of 3)

- [A-IDE : Agent-Integrated Denoising Experts](https://mlsanity.com/paper/2503.16780/) (2025)
- [Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research](https://mlsanity.com/paper/2506.13306/) (2025)
- [MoRE: A Mixture-of-Experts-Based Task-Adaptive End-to-End Network for Multimodal MRI Reconstruction](https://mlsanity.com/paper/2606.01784/) (2026)

## Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is an essential diagnostic tool in clinical settings, but its utility is often hindered by noise artifacts introduced during the imaging process. Effective denoising is critical for enhancing image quality while preserving anatomical structures. However, traditional denoising methods, which often assume uniform noise distributions, struggle to handle the non-uniform noise commonly present in MRI images. Building on prior multi-branch MRI denoising approaches, we introduce a fine-grained sparse mixture-of-experts framework for MRI image denoising. Our method decomposes each image into patch-based or segmentation-based regions, groups regions according to their learned feature similarity, and routes each region to a specialized denoising convolutional neural network. Our method demonstrates superior performance over state-of-the-art denoising techniques on both synthetic and real-world brain MRI datasets. Furthermore, we show that it generalizes effectively to unseen datasets, highlighting its robustness and adaptability.