Abstract: In view of problem that some present algorithms cannot effectively remove salt-and-pepper noise meanwhile preserving edges and details in the case of high noise density,a multi-directional weighted mean denoising algorithm based on two stage noise restoration was proposed.In the noise detection stage,the proposed algorithm firstly introduces a variance parameter to judge the gray level difference between current pixel and its neighborhood pixels,then designs the noise detector by combining the variance parameter and gray level extreme.In the noise restoration stage,a two stage restoration method is introduced to restore the gray value of noisy pixels.Firstly,the restoration method uses the improved adaptive median filter to carry out the first stage noise restoration,then divides all the noisy pixels into two types and applies different restoration skills to carry out the second stage noise restoration.One type of noisy pixel is further restored by the mean filter and the other type of noisy pixel is further restored by the multi-directional weighted mean filter.Experimental results show that the proposed algorithm outperforms many state-of-the-art filters in terms of image denoising and edge preservation.

Key words: Image denoising, Multi-directional weighted mean filter, Salt-and-pepper noise, Two stage restoration method, Variance parameter