Rician noise reduction in magnetic resonance images using adaptive non-local mean and guided image filtering

2016 ◽  
Vol 23 (3) ◽  
pp. 460-469 ◽  
Author(s):  
Muhammad Tariq Mahmood ◽  
Yeon-Ho Chu ◽  
Young-Kyu Choi
Keyword(s):  
Magnetic Resonance ◽  
Noise Reduction ◽  
Image Filtering ◽  
Magnetic Resonance Images ◽  
Rician Noise ◽  
Local Mean ◽  
Non Local ◽  
Guided Image Filtering

Noise reduction in magnetic resonance images using adaptive non‐local means filtering

2013 ◽  
Vol 49 (5) ◽  
pp. 324-326 ◽  
Author(s):  
B. Kang ◽  
O. Choi ◽  
J.D. Kim ◽  
D. Hwang
Keyword(s):  
Magnetic Resonance ◽  
Noise Reduction ◽  
Magnetic Resonance Images ◽  
Local Means ◽  
Non Local

scholarly journals Implementation of Iterative bilateral filtering for removal of Rician noise in MR images using FPGA

2020 ◽  
Vol 9 (3) ◽  
pp. 279-284
Keyword(s):  
Magnetic Resonance ◽  
Real Time ◽  
Visual Analysis ◽  
Similarity Index ◽  
Structural Similarity ◽  
Bilateral Filter ◽  
Magnetic Resonance Images ◽  
Rician Noise ◽  
Diagnostic Quality ◽  
Index Matrix

Magnetic resonance image noise reduction is important to process further and visual analysis. Bilateral filter is denoises image and also preserves edge. It proposes Iterative bilateral filter which reduces Rician noise in the magnitude magnetic resonance images and retains the fine structures, edges and it also reduces the bias caused by Rician noise. The visual and diagnostic quality of the image is retained. The quantitative analysis is based on analysis of standard quality metrics parameters like peak signal-to-noise ratio and mean structural similarity index matrix reveals that these methods yields better results than the other proposed denoising methods for MRI. Problem associated with the method is that it is computationally complex hence time consuming. It is not recommended for real time applications. To use in real time application a parallel implantation of the same using FPGA is proposed.

scholarly journals Non-local mean filtering algorithm based on deep learning

2018 ◽  
Vol 232 ◽  
pp. 03025
Author(s):  
Baozhong Liu ◽  
Jianbin Liu
Keyword(s):  
Deep Learning ◽  
Noise Reduction ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Denoising Method ◽  
Filtering Algorithm ◽  
Local Mean ◽  
Non Local ◽  
Reduction Effect ◽  
Mean Filtering

Aimed at the problem that the traditional image denoising algorithm is not effective in noise reduction, a new image denoising method is proposed. The method combines deep learning and non-local mean filtering algorithms to denoise the noisy image to obtain better noise reduction effect. By comparing with Gaussian filtering algorithm, median filtering algorithm, bilateral filtering algorithm and early non-local mean filtering algorithm, the noise reduction effect of the new algorithm is better than the traditional method and the peak signal to noise ratio is compared with the early non-local mean algorithm. The performance is better.

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