An effective self-adaptive mean filter for mixed noise

Abstract

The image is usually corrupted by Gauss noise and impulse noise simultaneously, and its quality will be reduced. Thus filtering image is important in image processing. The traditional mean filter cannot remove impulse noise effectively while preserve the details of image well. And the median filter cannot remove Gauss noise effectively. In this paper, we propose the self-adaptive mean filter to remove mixed noise of Gauss noise and impulse noise. Firstly, dividing pixels of image into good pixels and corrupted pixels based on whether there are noises in their small neighborhoods. And the greyscale value of good pixels are output directly. Secondly, for corrupted pixels, removing Gauss noises and impulse noises respectively based on characteristics of different noise. The results demonstrate that the self-adaptive mean filter can eliminate mixed noise of different density better and preserve the details of image better comparing with the mean filter or the median filter for mixed noise.