Speech Random Impulse Noise Elimination Method Based on Robust PCA Inexact ALM Algorithm

2021 ◽  
pp. 278-285
Author(s):  
Haiyun Zhang ◽  
Jian Dong ◽  
Jiancheng Zhou ◽  
Li Zhang ◽  
Pengjun Hu ◽  
...  
Keyword(s):  
Impulse Noise ◽  
Robust Pca ◽  
Noise Elimination ◽  
Elimination Method ◽  
Random Impulse

A fuzzy filter for the removal of random impulse noise in image sequences

2011 ◽  
Vol 29 (6) ◽  
pp. 407-419 ◽  
Author(s):  
Tom Mélange ◽  
Mike Nachtegael ◽  
Stefan Schulte ◽  
Etienne E. Kerre
Keyword(s):  
Impulse Noise ◽  
Image Sequences ◽  
Fuzzy Filter ◽  
Random Impulse

scholarly journals Image mixed gaussian and impulse noise elimination based on sparse representation model

2021 ◽  
Vol 23 (3) ◽  
pp. 1440
Author(s):  
Ahmed Abdulqader Hussein ◽  
Sabahaldin A. Hussain ◽  
Ahmed Hameed Reja
Keyword(s):  
Sparse Representation ◽  
Image Denoising ◽  
Impulse Noise ◽  
Heavy Tails ◽  
Noise Distribution ◽  
Self Similarity ◽  
Noise Elimination ◽  
Variational Framework ◽  
Mixed Noise ◽  
State Of Art

<p>A modified mixed Gaussian plus impulse image denoising algorithm based on weighted encoding with image sparsity and nonlocal self-similarity priors regularization is proposed in this paper. The encoding weights and the priors imposed on the images are incorporated into a variational framework to treat more complex mixed noise distribution. Such noise is characterized by heavy tails caused by impulse noise which needs to be eliminated through proper weighting of encoding residual. The outliers caused by the impulse noise has a significant effect on the encoding weights. Hence a more accurate residual encoding error initialization plays the important role in overall denoising performance, especially at high impulse noise rates. In this paper, outliers free initialization image, and an easier to implement a parameter-free procedure for updating encoding weights have been proposed. Experimental results demonstrate the capability of the proposed strategy to recover images highly corrupted by mixed Gaussian plus impulse noise as compared with the state of art denoising algorithm. The achieved results motivate us to implement the proposed algorithm in practice.</p>

Scattering Noise Elimination Method for Improving Spectral Matching Accuracy

2019 ◽  
Vol 56 (2) ◽  
pp. 022401
Author(s):  
王展 Wang Zhan ◽  
王可 Wang Ke ◽  
王伟超 Wang Weichao
Keyword(s):  
Matching Accuracy ◽  
Noise Elimination ◽  
Spectral Matching ◽  
Elimination Method ◽  
Scattering Noise

Adaptive Median Filtering for Impulse Noise Elimination in Real-Time TV Signals

1987 ◽  
Vol 35 (6) ◽  
pp. 646-652 ◽  
Author(s):  
S. Perlman ◽  
S. Eisenhandler ◽  
P. Lyons ◽  
M. Shumila
Keyword(s):  
Real Time ◽  
Impulse Noise ◽  
Median Filtering ◽  
Noise Elimination

Regression-Based Neuro-Fuzzy Network Trained by ABC Algorithm for High-Density Impulse Noise Elimination

2020 ◽  
Vol 28 (6) ◽  
pp. 1084-1095 ◽  
Author(s):  
Abdullah Caliskan ◽  
Zeynel Abidin Cil ◽  
Hasan Badem ◽  
Dervis Karaboga
Keyword(s):  
Impulse Noise ◽  
High Density ◽  
Noise Elimination ◽  
Abc Algorithm ◽  
Neuro Fuzzy ◽  
Fuzzy Network

An Improved Mean Filter Algorithm for Impulse Noise Elimination

2014 ◽  
Vol 936 ◽  
pp. 2281-2285
Author(s):  
Wang Tang ◽  
Jin Tang
Keyword(s):  
Computer Simulation ◽  
Impulse Noise ◽  
Window Size ◽  
Impulse Wave ◽  
Noise Detection ◽  
Noise Elimination ◽  
Mean Filter ◽  
Detection Window ◽  
Improved Algorithm

Since the standard mean filter has some drawbacks, an improved mean filter is proposed in this paper to overcome them. In this improved algorithm, noise detection is used to find the noise which is caused by impulse wave in the image. Based on the result of noise detection, window size would be selected in the improved mean filter to minimize the influence of the noise. According to the result of computer simulation, it shows that the improved mean filter is much more efficient than the standard mean filter in eliminating the noise as well as in protecting the details of the image. It also shows its advantage in filtering speed.

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