Thresholded median filter for the impulsive noise removal in digital images

2014 ◽  
Author(s):  
Bogdan Smolka ◽  
Adam Andrzejczak ◽  
Pawel Nabialkowski ◽  
Adam Nelip
Keyword(s):  
Digital Images ◽  
Impulsive Noise ◽  
Median Filter ◽  
Noise Removal

A Spatial Median Filter for noise removal in digital images

2008 ◽  
Author(s):  
James C. Church ◽  
Yixin Chen ◽  
Stephen V. Rice
Keyword(s):  
Digital Images ◽  
Median Filter ◽  
Noise Removal ◽  
Spatial Median

scholarly journals Decision-Based Marginal Total Variation Diffusion for Impulsive Noise Removal in Color Images

2017 ◽  
Vol 2017 ◽  
pp. 1-18
Author(s):  
Hongyao Deng ◽  
Qingxin Zhu ◽  
Xiuli Song
Keyword(s):  
Total Variation ◽  
Color Image ◽  
Impulsive Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Color Images ◽  
Natural Image ◽  
Noise Characteristics ◽  
Switching Median Filter ◽  
Vector Median

Impulsive noise removal for color images usually employs vector median filter, switching median filter, the total variation L1 method, and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and thus are suitable only for images with low density noise. A marginal method to reduce impulsive noise is proposed in this paper that overcomes this limitation that is based on the following facts: (i) each channel in a color image is contaminated independently, and contaminative components are independent and identically distributed; (ii) in a natural image the gradients of different components of a pixel are similar to one another. This method divides components into different categories based on different noise characteristics. If an image is corrupted by salt-and-pepper noise, the components are divided into the corrupted and the noise-free components; if the image is corrupted by random-valued impulses, the components are divided into the corrupted, noise-free, and the possibly corrupted components. Components falling into different categories are processed differently. If a component is corrupted, modified total variation diffusion is applied; if it is possibly corrupted, scaled total variation diffusion is applied; otherwise, the component is left unchanged. Simulation results demonstrate its effectiveness.

scholarly journals An Empirical Evaluation of the Parameters of Trilateral Filter for Noise Removal Implementation on Gaussian and Impulsive Noise

2017 ◽  
Vol 11 (2) ◽  
pp. 190-202
Author(s):  
Vorapoj Patanavijit ◽  
Kornkamol Thakulsukanant
Keyword(s):  
Gaussian Noise ◽  
Impulsive Noise ◽  
Median Filter ◽  
Power Level ◽  
Empirical Evaluation ◽  
Poor Performance ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Superior Performance ◽  
Trilateral Filter

Since 1998, the Bilateral filter (BF) is worldwide accepted for its performance in practical point of view under Gaussian noise however the Bilateral filter has a poor performance for impulsive noise. Based on the combining of the Rank-Ordered Absolute Differences (ROAD) detection technique and the Bilateral filter for automatically reducing or persecuting of impulsive and Gaussian noise, this Trilateral filter (TF) has been proposed by Roman Garnett et al. since 2005 but the Trilateral filter efficiency is rest absolutely on spatial, radiometric, ROAD and joint impulsivity variance. Hence, this paper computationally determines the optimized values of the spatial, radiometric, ROAD and joint impulsivity variance of the Trilateral filter (TF) for maximum performance. In the experiment, nine noisy standard images (Girl-Tiffany, Pepper, Baboon, House, Resolution, Lena, Airplain, Mobile and Pentagon) under both five power-level Gaussian noise setting and five density impulsive noise setting, are used for estimating optimized parameters of Trilateral filter and for demonstrating the its overall performance, which is compared with classical noise removal techniques such as median filter, linear smoothing filter and Bilateral filter (BF). From the noise removal results of empirically experiments with the highest PSNR criterion, the trilateral filter with the optimized parameters has the superior performance because the ROAD variance and joint impulsivity variance can be statistically analyzed and estimated for each experimental case.

scholarly journals AN UNSUPERVISED NEURAL NETWORK FOR IMPULSIVE NOISE REMOVAL IN DIGITAL IMAGES

2001 ◽  
Vol 9 (ASAT Conference, 8-10 May 2001) ◽  
pp. 1-17
Author(s):  
FAYED A. ◽  
MAHMOUD A.
Keyword(s):  
Neural Network ◽  
Digital Images ◽  
Impulsive Noise ◽  
Noise Removal ◽  
Unsupervised Neural Network

Multi scale sequential median filter for high percentage impulsive noise removal

2017 ◽  
Author(s):  
Dongjie Tan ◽  
Haoyin Lv
Keyword(s):  
Impulsive Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Multi Scale
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