A Highly Effective Adaptive Switching Mean Filter Algorithm for Salt and Pepper Noise Removal

Abstract

This paper presents an efficient three-stage adaptive switching mean filter to remove salt-and-pepper impulse noise from highly corrupted images. Firstly, the noise detection stage is to detect pixels as “noise pixels” and “noise-free pixels”. The detected “noise pixels” will then be subjected to the second stage which is the noise cancellation, while “noise-free pixels” are retained and left unchanged. The method adaptively changes the size of the filtering window based on the number of the “noise-free pixels” in the neighborhood. For the filtering, only “noise-free pixels” in the window are considered to find the mean value. If this value is not available in the maximum window size, the last processed pixel value is used as the replacement. In the third stage, this algorithm utilizes previously processed neighboring pixel values to get better image quality as the really processed noise pixel used as a noise –free pixel for the next noisy pixel processing. Experimental results clearly show that the proposed algorithm outperforms many of the existing methods in terms of visual quality and quantitative measures. The advantage of the proposed method is that it works well for high-density salt & pepper noise even up to a noise percentage of 95%. General Terms Image processing, Image filtering, Noise reduction.