Impulse Noise Removal Using Directional Difference Based Noise Detector and Adaptive Weighted Mean Filter

2009 ◽  
Vol 16 (4) ◽  
pp. 295-298 ◽  
Author(s):  
Xuming Zhang ◽  
Youlun Xiong
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Weighted Mean ◽  
Impulse Noise Removal ◽  
Mean Filter ◽  
Directional Difference

A weighted mean filter with spatial-bias elimination for impulse noise removal

2015 ◽  
Vol 46 ◽  
pp. 164-174 ◽  
Author(s):  
Cengiz Kandemir ◽  
Cem Kalyoncu ◽  
Önsen Toygar
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Spatial Bias ◽  
Weighted Mean ◽  
Impulse Noise Removal ◽  
Mean Filter

scholarly journals Mixed Noise Removal Algorithm Combining Adaptive Directional Weighted Mean Filter and Improved Adaptive Anisotropic Diffusion Model

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Hongjin Ma ◽  
Yufeng Nie
Keyword(s):  
Diffusion Model ◽  
Anisotropic Diffusion ◽  
Impulse Noise ◽  
Noise Removal ◽  
Image Features ◽  
Weighted Mean ◽  
Optimal Direction ◽  
Mean Filter ◽  
Mixed Noise ◽  
Mixed Noise Removal

A mixed noise removal algorithm combining adaptive directional weighted mean filter and improved adaptive anisotropic diffusion model is proposed. Firstly, a noise classification method is introduced to divide all pixels into two types as the pixels corrupted by impulse noise and the pixels corrupted by Gaussian noise. Then an adaptive directional weighted mean filter is developed to remove impulse noise, which can adaptively select the optimal direction template from twelve direction templates and replace the gray level of each impulse noise corrupted pixel by the weighted mean gray level of pixels on the optimal direction template. Finally, an improved adaptive anisotropic diffusion model is developed to remove Gaussian noise in the initial denoised image, which can finely classify image features as smooth regions, edges, corners, and isolated noises by characteristic parameters and variance parameter and conduct adaptive diffusion for different image features by designing reasonable eigenvalues of diffusion tensor. A large number of experimental results show that the proposed algorithm outperforms many existing main mixed noise removal methods in terms of image denoising and detail preservation.

A Morphological Mean Filter for Impulse Noise Removal

2015 ◽  
pp. 1-1 ◽  
Author(s):  
Po-Hsiung Lin ◽  
Bo-Hao Chen ◽  
Fan-Chieh Cheng ◽  
Shih-Chia Huang
Keyword(s):  
Impulse Noise ◽  
Noise Removal ◽  
Impulse Noise Removal ◽  
Mean Filter
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