Image Fusion Using SGNN

There have been some reports on approaches to image fusion based on neural networks recently. But these approaches have some shortages, such as being complex on computing, and having to have their network structures and many parameters to be set by users in advance. Self-generating neural network (SGNN) is a self-organization neural network, whose network structures and parameters need not to be set by users, and its learning process needs no iteration. In this paper, an approach to image fusion using SGNN is proposed. This newly proposed approach consists of three steps: ①pre-processing of the images. It removes the noises in the images by discrete wavelet transforms; ②clustering pixels using SGNN; and ③ fusing images using fuzzy logic algorithms. The approach has advantages of being wieldy used by users and having high computing efficiency. The experimental results demonstrate that the MSE (mean square error) reduced by this proposed approach decreases 30%～60% than that by Laplacian pyramid and discrete wavelet transform approaches.