# MRI denoising using Deep Learning and Non-local averaging

arXiv: https://arxiv.org/abs/1911.04798 · HTML: https://mlsanity.com/paper/1911.04798/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 6 of 6)

- [MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion](https://mlsanity.com/paper/2203.12621/) (2022)
- [SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI](https://mlsanity.com/paper/2111.07220/) (2021)
- [An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision](https://mlsanity.com/paper/2209.04646/) (2022)
- [Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising](https://mlsanity.com/paper/2407.15799/) (2024)
- [Untrained Perceptual Loss for image denoising of line-like structures in MR images](https://mlsanity.com/paper/2411.05884/) (2024)
- [An Attention-Based Denoising Model for Diffusion Weighted Imaging](https://mlsanity.com/paper/2606.03903/) (2026)

## Abstract (arXiv)

This paper proposes a novel method for automatic MRI denoising that exploits last advances in deep learning feature regression and self-similarity properties of the MR images. The proposed method is a two-stage approach. In the first stage, an overcomplete patch-based convolutional neural network blindly removes the noise without specific estimation of the local noise variance to produce a preliminary estimation of the noise-free image. The second stage uses this preliminary denoised image as a guide image within a rotationally invariant non-local means filter to robustly denoise the original noisy image. The proposed approach has been compared with related state-of-the-art methods and showed competitive results in all the studied cases while being much faster than comparable filters. We present a denoising method that can be blindly applied to any type of MR image since it can automatically deal with both stationary and spatially varying noise patterns.