Selective Mean Filtering for Reducing Impulse Noise in Digital Color Images

The interest of this paper is in reduction of impulse noise in digital color images. The two main methods used for noise reduction in images are the mean and median filters. These techniques operate by replacing the test pixel in a chosen window by a new filtered pixel value. The window is made to iteratively slide across the entire image to reconstruct a new noise reduced image. The mean filters suffer from the effect of smoothing out color contrast and edges due to leveraging the unrepresentative pixels in the filtering process. The vector median filter and its variants overcome this problem by considering only the most representative pixel in the chosen window. The most representative pixel, i.e. the pixel that is of highest conformity to take the place of the test pixel, is determined by minimizing the aggregate distance from one pixel to every other pixel in the window. The problem in these median filtering approaches is that only one pixel is treated as representative of all the pixels in the chosen window. This conjecture could lead to information loss due to marginalizing other pixels that also are representative of the center pixel. In this paper, we propose a selective mean filtering process to overcome the said problem. The key idea here is to determine the most representative pixels in the window using the method of aggregate distances and then compute the mean of these pixels. This approach will perform better than the vector median filters as now a set of representative pixels are leveraged into the filtering process. Simulation results show that the proposed method performs better than the conventional vector median filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. Moreover, the method is tested on real MRI scan images in successfully reducing impulse noise for improved medical diagnosis.