Image fusion based on the self-organizing feature map neural networks

This paper presents a new image data fusion scheme based on the self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using two-dimensional self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2) utilizing fuzzy logic, which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).