MRI denoising using Deep Learning and Non-local averaging

11/12/2019 ∙ by Manjon Jose V., et al. ∙ 0

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.



There are no comments yet.


page 7

page 15

page 17

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.