scholarly journals Mixed-Model Noise Removal in 3D MRI via Rotation-and-Scale Invariant Non-Local Means

2019 ◽  
pp. 33-41
Author(s):  
Xiangyuan Liu ◽  
Quansheng Liu ◽  
Zhongke Wu ◽  
Xingce Wang ◽  
Jose Pozo Sole ◽  
...  
Keyword(s):  
Mixed Model ◽  
Noise Removal ◽  
3D Mri ◽  
Scale Invariant ◽  
Local Means ◽  
Non Local

Fuzzy similarity based non local means filter for Rician noise removal

2014 ◽  
Vol 74 (15) ◽  
pp. 5533-5556 ◽  
Author(s):  
Muhammad Sharif ◽  
Ayyaz Hussain ◽  
Muhammad Arfan Jaffar ◽  
Tae-Sun Choi
Keyword(s):  
Noise Removal ◽  
Rician Noise ◽  
Local Means ◽  
Fuzzy Similarity ◽  
Non Local

Region-based non-local means algorithm for noise removal

2011 ◽  
Vol 47 (20) ◽  
pp. 1125 ◽  
Author(s):  
W.L. Zeng ◽  
X.B. Lu
Keyword(s):  
Noise Removal ◽  
Local Means ◽  
Non Local

Natural image noise removal using non local means and hidden Markov models in stationary wavelet transform domain

2017 ◽  
Vol 77 (15) ◽  
pp. 20065-20086 ◽  
Author(s):  
Asem Khmag ◽  
Syed Abdul Rahman Al Haddad ◽  
Ridza Azri Ramlee ◽  
Noraziahtulhidayu Kamarudin ◽  
Fahad Layth Malallah
Keyword(s):  
Wavelet Transform ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Noise Removal ◽  
Natural Image ◽  
Transform Domain ◽  
Stationary Wavelet Transform ◽  
Local Means ◽  
Non Local

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

2020 ◽  
Vol 13 (4) ◽  
pp. 14-31
Author(s):  
Nikita Joshi ◽  
Sarika Jain ◽  
Amit Agarwal
Keyword(s):  
Magnetic Resonance ◽  
Total Variation ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
Variation Method ◽  
Mr Images ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

Noise removal in medical mammography images using fast non-local means denoising algorithm for early breast cancer detection: a phantom study

Optik ◽  
2019 ◽  
Vol 180 ◽  
pp. 569-575 ◽  
Author(s):  
Sungtaek Lee ◽  
Seong Jin Park ◽  
Ji Min Jeon ◽  
Mi-Hwa Lee ◽  
Dae Yeon Ryu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Cancer Detection ◽  
Noise Removal ◽  
Breast Cancer Detection ◽  
Phantom Study ◽  
Local Means ◽  
Non Local
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