A New Impulse Noise Filtering Algorithm Based on a Neuro-Fuzzy Network

2013 ◽  
pp. 41-56 ◽  
Author(s):  
Yueyang Li ◽  
Haichi Luo ◽  
Jun Sun
Keyword(s):  
Impulse Noise ◽  
Noise Filtering ◽  
Filtering Algorithm ◽  
Neuro Fuzzy ◽  
Fuzzy Network

A neuro-fuzzy network based impulse noise filtering for gray scale images

2014 ◽  
Vol 127 ◽  
pp. 190-199 ◽  
Author(s):  
Yueyang Li ◽  
Jun Sun ◽  
Haichi Luo
Keyword(s):  
Impulse Noise ◽  
Noise Filtering ◽  
Gray Scale ◽  
Neuro Fuzzy ◽  
Fuzzy Network

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

Impulse Noise Filtering Algorithm Based on Dual Threshold Criterion

2013 ◽  
Vol 437 ◽  
pp. 849-852 ◽  
Author(s):  
Yu Lan Wei ◽  
Bing Li ◽  
Jian Dong Li ◽  
Ying Ying Fan
Keyword(s):  
Impulse Noise ◽  
Surface Defect ◽  
Median Filter ◽  
Noise Filtering ◽  
Median Filtering ◽  
Filtering Algorithm ◽  
Obvious Advantage ◽  
Noise Point ◽  
Threshold Criterion ◽  
Good For

In order to filter the impulse noise existing in the weld surface defect images, the traditional median filtering will dim image, even destroy some details in the image, We put forward a new filtering algorithm based on dual threshold Criterion. This way distinguishes the noise point and signal location in the first, and it only doing median filter to the noise point. Lastly, it solves the image’s boundary. We can find it when compared to the traditional median filtering and modified extremum median filtering, the way in this article can be good for filtration, and can keep the image’s details, which have obvious advantage.

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