An Experimental Study on MRI Denoising with Existing Image Denoising Methods

被引：0

作者：

Chen, Guang Yi ^{[1
]}

Xie, Wenfang ^{[2
]}

Krzyzak, Adam ^{[1
]}

机构：

[1] Concordia Univ, Dept Comp Sci & Software Engn, Montreal, PQ H3G 1M8, Canada

[2] Concordia Univ, Dept Mech Ind & Aerosp Engn, Montreal, PQ H3G 1M8, Canada

来源：

ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, ICIC 2023, PT II | 2023年 / 14087卷

关键词：

Magnetic resonance imaging (MRI); Denoising; Block matching and 3D filtering (BM3D); Denoising convolutional neural networks (DnCNN); WAVELET;

D O I：

10.1007/978-981-99-4742-3_35

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we perform a systematical study on existing 2D denoising methods for reducing the noise in magnetic resonance imaging (MRI). We conduct experiments on six MRI images with the following denoising methods: wiener2, wavelet-based denoising, bivariate shrinkage (BivShrink), SURELET, Non-local Means (NLM), block matching and 3D filtering (BM3D), denoising convolutional neural networks (DnCNN) and weighted nuclear norm minimization (WNNM). Based on our experiments, the BM3D and the WNNM are the best two methods for MRI image denoising. Nevertheless, the WNNM is the slowest in term of CPU computational time. As a result, it is preferable to choose the BM3D for MRI denoising.

引用

页码：429 / 437

页数：9

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