Extending the Application of Conditional Signal Adaptive Median Filter to Impulsive Noise

2006 ◽  
Author(s):  
Mohammed Hashem ◽  
Abdel-moneim Wahdan ◽  
Ashraf Salem ◽  
Tamer Mostafa
Keyword(s):  
Impulsive Noise ◽  
Median Filter ◽  
Conditional Signal ◽  
Adaptive Median Filter

scholarly journals Computational scrutiny of image denoising method found on DBAMF under SPN surrounding

2020 ◽  
Vol 10 (4) ◽  
pp. 4109
Author(s):  
Vorapoj Patanavijit
Keyword(s):  
Digital Images ◽  
Impulsive Noise ◽  
Rank Order ◽  
Median Filter ◽  
Computational Simulation ◽  
Absolute Difference ◽  
Gaussian Filter ◽  
Denoising Method ◽  
Adaptive Median Filter ◽  
Denoised Image

Traditionally, rank order absolute difference (ROAD) has a great similarity capacity for identifying whether the pixel is SPN or noiseless because statistical characteristic of ROAD is desired for a noise identifying objective. As a result, the decision based adaptive median filter (DBAMF) that is found on ROAD technique has been initially proposed for eliminating an impulsive noise since 2010. Consequently, this analyzed report focuses to examine the similarity capacity of denoising method found on DBAMF for diverse SPN Surrounding. In order to examine the denoising capacity and its obstruction of the denoising method found on DBAMF, the four original digital images, comprised of Airplane, Pepper, Girl and Lena, are examined in these computational simulation for SPN surrounding by initially contaminating the SPN with diverse intensity. Later, all contaminated digital images are denoised by the denoising method found on DBAMF. In addition, the proposed denoised image, which is computed by this DBAMF denoising method, is confronted with the other denoised images, which is computed by Standard median filter (SMF), Gaussian Filter and Adaptive median filter (AMF) for demonstrating the DBAMF capacity under subjective measurement aspect.

Application of Improved Back Propagation Neural Network for the Recognition of Composite Insulator Hydrophobicity Grade

2013 ◽  
Vol 373-375 ◽  
pp. 1155-1158
Author(s):  
Kang Yan ◽  
Zhong Yuan Zhang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Median Filter ◽  
Back Propagation ◽  
Histogram Equalization ◽  
Back Propagation Neural Network ◽  
Threshold Method ◽  
Safe Operation ◽  
Composite Insulator ◽  
Adaptive Median Filter

The detection of hydrophobicity is an important way to evaluate the performance of composite insulator, which is helpful to the safe operation of composite insulator. In this paper, the image processing technology and Back Propagation neural network is introduced to recognize the composite insulator hydrophobicity grade. First, hydrophobic image is preprocessed by histogram equalization and adaptive median filter, then the image was segmented by Ostu threshold method, and four features associated with hydrophobicity are extracted. Finally, the improved Back Propagation neural network is adopted to recognize composite insulator hydrophobicity grade. The experimental results show that the improved Back Propagation neural network can accurately recognize the composite insulator hydrophobicity

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