scholarly journals Improved preclassification non local-means (IPNLM) for filtering of grayscale images degraded with additive white Gaussian noise

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Isabel V Hernández-Gutiérrez ◽  
Francisco J Gallegos-Funes ◽  
Alberto J Rosales-Silva
Keyword(s):  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
White Gaussian Noise ◽  
Local Means ◽  
Non Local ◽  
Grayscale Images

A Non-Local Image Denoising Technique Using Adaptive Filter Parameter

2014 ◽  
Vol 556-562 ◽  
pp. 4839-4842
Author(s):  
Song Yuan Tang
Keyword(s):  
Image Denoising ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Wiener Filter ◽  
White Gaussian Noise ◽  
Filter Parameter ◽  
Iterative Computation ◽  
Local Mean ◽  
Non Local

This paper proposes a method to obtain the optimal filter parameter of the non-local mean (NLM) algorithm. The parameter is assumed to be a function of the variance of the additive white Gaussian noise and is adaptive estimated. The initialization of the variance of the additive white Gaussian noise is estimated by Wiener filter. Then the NLM filter is used to adaptively estimate the noise variance. The image denoising is an iterative computation till the parameter convergence. Experiments show that the proposed method can improve the quality of the denoised images efficiently.

An Improvement of Non-Local Means Denoising Method in the Presence of Large Noise

2012 ◽  
Vol 263-266 ◽  
pp. 223-226
Author(s):  
Musab Elkheir Salih ◽  
Xu Ming Zhang ◽  
Ming Yue Ding
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Principal Component ◽  
Singular Value ◽  
Kernel Principal Component Analysis ◽  
Denoising Method ◽  
Local Means ◽  
Non Local ◽  
Value Decomposition

The performance of singular value decomposition (SVD) based nonlocal mean (NLM) denoising method degrades when the noise is high. This paper describes an approach of an improvement of NLM denoising when the noise is large. Instead of SVD, we combine the kernel principal component analysis (KPCA) with NLM. It is demonstrated in terms of peak signal to noise ratio (PSNR) in decibels (dB) that the NLM denoising method is improved using various test images corrupted by large additive white Gaussian noise (AWGN)

Performance analysis of secure communications over correlated slow-fading additive white Gaussian noise channels

2019 ◽  
Vol 32 (12) ◽  
pp. e4022 ◽  
Author(s):  
Ali Mohammad Khodadoust ◽  
Javad Khodadoust ◽  
Azeem Irshad ◽  
Shehzad Ashraf Chaudhry
Keyword(s):  
Performance Analysis ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
White Gaussian Noise ◽  
Secure Communications ◽  
Slow Fading ◽  
Gaussian Noise Channels
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