High Density Salt and Pepper Noise Removal in Images Using Adapted Decision Based Unsymmetrical Trimmed Mean Filter Cascaded with Gaussian filter

2016 ◽  
Author(s):  
Prakriti Thakur ◽  
Keyword(s):  
Noise Removal ◽  
High Density ◽  
Gaussian Filter ◽  
Salt And Pepper Noise ◽  
Trimmed Mean ◽  
Mean Filter ◽  
Salt And Pepper

scholarly journals An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Symmetry ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1990
Author(s):  
Fengyu Chen ◽  
Minghui Huang ◽  
Zhuxi Ma ◽  
Yibo Li ◽  
Qianbin Huang
Keyword(s):  
Noise Removal ◽  
High Density ◽  
Salt And Pepper Noise ◽  
Weighted Mean ◽  
Real Time Processing ◽  
Noise Density ◽  
Mean Filter ◽  
Execution Speed ◽  
Salt And Pepper

Salt-and-pepper noise, which is often introduced by sharp and sudden disturbances in the image signal, greatly reduces the quality of images. Great progress has been made for the salt-and-pepper noise removal; however, the problem of image blur and distortion still exists, and the efficiency of denoising requires improvement. This paper proposes an iterative weighted-mean filter (IWMF) algorithm in detecting and removing high-density salt-and-pepper noise. Three steps are required to implement this algorithm: First, the noise value and distribution characteristics were used to identify the noise pixels, effectively improving the accuracy of noise detection. Second, a weighted-mean filter was applied to the noisy pixels. We adopted an un-fixed shape symmetrical window with better detail preservation ability. Third, this method was performed iteratively, avoiding the streak effect and artifacts in high noise density. The experimental results showed that IWMF outperformed other state-of-the-art filters at various noise densities, both in subjective visualization and objective digital measures. The extremely fast execution speed of this method is quite suitable for real-time processing.

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