Abstract
To deal with the problem of restoring images degraded with Gaussian white noise, the mean and adaptive Wiener filters are the most common methods to be implemented. Although these methods are both lowpass in character, they yield different results on the same problem. The mean filter reduces more noise than the adaptive Wiener but also blurs the image edges, whereas the adaptive Wiener filter can preserve edge sharpness but reduces less noise than the mean filter. Instead of trying to design a single mathematical technique to have the advantages of both methods, which is usually theoretically difficult, we propose an alternative solution to this image restoration by fusing multiple image filters using the mean, Sobel, and adaptive Wiener filters. Performance of the fusion algorithm is based on both redundant and complementary information provided by different filters. Several experimental results show the effective application of the proposed approach.