Random Valued Impulse Noise Detection Using Fuzzy c-Means Clustering Technique

2021 ◽  
pp. 397-405
Author(s):  
Aritra Bandyopadhyay ◽  
Kaustuv Deb ◽  
Atanu Das ◽  
Rajib Bag
Keyword(s):  
Impulse Noise ◽  
Noise Detection ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Impulse Noise Detection

A Denoising Mixed Noise Method Based on Median Filter and Lifting Wavelet Technology

2013 ◽  
Vol 411-414 ◽  
pp. 1546-1551 ◽  
Author(s):  
Zhong Tao Qiao ◽  
Feng Qi Gao ◽  
Guang Long Wang ◽  
Liang Liang Chang
Keyword(s):  
Impulse Noise ◽  
Median Filter ◽  
Detection Algorithm ◽  
Noise Detection ◽  
Image Digitization ◽  
Mixed Noise ◽  
The Common ◽  
Lifting Wavelet ◽  
Impulse Noise Detection ◽  
Suppress Noise

In image digitization and transmission, images often suffer contamination inevitably. The noises in images often consist of Gaussian noise and impulse noise. The common denoising algorithms are capable of removing single one of them. In order to remove those two types of noise, a composite algorithm is proposed. Firstly, based on median filter, an impulse noise detection algorithm is used to filter impulse noise. Secondly, adaptive directional lifting wavelet (ADL) and normal lifting wavelet is combined to suppress noise from image signal and protect the texture edge from loss simultaneously. Meanwhile an improved half-soft threshold is used for normal lifting wavelet. At last, simulations show that this technology can suppress Gaussian and impulse noise in image efficiently．

High Density Impulse Noise Detection using Fuzzy C-means Algorithm

2016 ◽  
Vol 66 (1) ◽  
pp. 30
Author(s):  
Isha Singh ◽  
Om Prakash Verma
Keyword(s):  
Impulse Noise ◽  
Image Data ◽  
Window Size ◽  
High Density ◽  
Satellite Imaging ◽  
Fuzzy C Means ◽  
Efficient Detection ◽  
A New Technique ◽  
Impulse Noise Detection ◽  
Fuzzy C Means Algorithm

<p>A new technique for detecting the high density impulse noise from corrupted images using Fuzzy C-means algorithm is proposed. The algorithm is iterative in nature and preserves more image details in high noise environment. Fuzzy C-means is initially used to cluster the image data. The application of Fuzzy C-means algorithm in the detection phase provides an optimum classification of noisy data and uncorrupted image data so that the pictorial information remains well preserved. Experimental results show that the proposed algorithm significantly outperforms existing well-known techniques. Results show that with the increase in percentage of noise density, the performance of the algorithm is not degraded. Furthermore, the varying window size in the two detection stages provides more efficient results in terms of low false alarm rate and miss detection rate. The simple structure of the algorithm to detect impulse noise makes it useful for various applications like satellite imaging, remote sensing, medical imaging diagnosis and military survillance. After the efficient detection of noise, the existing filtering techniques can be used for the removal of noise.</p><p><strong>Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 30-36, DOI: http://dx.doi.org/10.14429/dsj.66.8722</strong></p>

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