A radial basis function (RBF)neural network based image fusion restoration approach for multiple spectrum images is proposed and investigated. The spatial resolution improvement of images with poor resolution can be achieved by fusing the images of various resolutions. The simulated experimental results show the effectiveness of the method. The image quality is improved by fusing the correlative and redundant information between images. The modified algorithms ensure quick training and mapping precision of the networks. The research also illustrates that using multi-channel information is more effective than using a single one and the scenario conducted has advantages over conventional methods.