MRI denoising using Deep Learning and Non-local averaging

Autor:	Manjon, Jose V., Coupe, Pierrick
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Mathematics - Numerical Analysis
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1911.04798 Zobrazit plný text záznamu View this record from Arxiv