Uncorrelated Weighted Median Filtering for Noise Removal in SuperDARN

2014 ◽  
Author(s):  
Clarence Goh ◽  
John C. Devlin ◽  
Dennis Deng ◽  
Andrew McDonald ◽  
Muhammad Ramlee Kamarudin
Keyword(s):  
Noise Removal ◽  
Median Filtering ◽  
Weighted Median

Adaptive Median Filtering Based on Unsupervised Classification of Pixels

2013 ◽  
pp. 273-296 ◽  
Author(s):  
J. K. Mandal ◽  
Somnath Mukhopadhyay
Keyword(s):  
Clustering Algorithm ◽  
Digital Images ◽  
Unsupervised Classification ◽  
Median Filtering ◽  
Adaptive Window ◽  
Novel Approach ◽  
Weighted Median ◽  
Window Selection ◽  
Filtering Approach

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

Adaptive weighted median filtering for reduced blurring when fusing co-registered fast helical CT images

2017 ◽  
Vol 3 (6) ◽  
pp. 067002
Author(s):  
D O’Connell ◽  
D H Thomas ◽  
T H Dou ◽  
E Aliotta ◽  
J H Lewis ◽  
...  
Keyword(s):  
Helical Ct ◽  
Ct Images ◽  
Median Filtering ◽  
Weighted Median

Noise removal in three-fringe photoelasticity by median filtering

2009 ◽  
Vol 47 (11) ◽  
pp. 1226-1230 ◽  
Author(s):  
Venketesh N. Dubey ◽  
Gurtej S. Grewal
Keyword(s):  
Noise Removal ◽  
Median Filtering

Polynomial weighted median filtering

2006 ◽  
Vol 54 (2) ◽  
pp. 636-650 ◽  
Author(s):  
K.E. Barner ◽  
T.C. Aysal
Keyword(s):  
Median Filtering ◽  
Weighted Median

scholarly journals A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

2020 ◽  
Vol 9 (4) ◽  
pp. 1305-1309
Keyword(s):  
Impulse Noise ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Noise Removal ◽  
Spatial Domain ◽  
Median Filtering ◽  
Impulse Noise Removal ◽  
Noise Density ◽  
Improved Accuracy ◽  
Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

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