A New Efficient Algorithm to Remove High Density Gaussian Noise with Edge Preservation

Abstract

In this paper a new algorithm is proposed to remove Gaussian noise with edge preservation. The function of the proposed algorithm is to first find the pixel values along the boundary of the filtering window and then calculate its variance. If this variance is less than a threshold specified, then the corrupted pixel is replaced by the mean of the inside pixels from the filtering window after sorting and trimming. Experimental results shows that the proposed algorithm outperforms with significant improvement in image quality than the arithmetic mean, alpha-trimmed mean filter, wiener filter, K-means filter and adaptive window based method. The proposed method removes the Gaussian noise very effectively even at a noise variance as high as 40 with edge preservation.