The Image Fusion Algorithm Based on Rough Neural Network

Abstract

This paper describes rough neural network which consists of a combination of rough neurons and conventional neurons. Rough neurons use pairs of upper and lower bounds as values for input and output. In some practical situations, it is preferable to develop prediction models that use ranges as values for input and/or output variables. Integrating rough set theory with neural network theory, a novel information fusion method based on rough neural network is proposed to fuse the different-source images in agricultural robot. It is used to fuse infrared and visible images in order to take full advantage of the complementary information between infrared and visible images. Experimental results show that the fusion effect and speed are both better than standard wavelet transform and the conventional neural network.