Image Fusion Algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G

Abstract

In recent years, in Space-Ground-Sea Wireless Networks, the rapid development of image recognition also promotes the development of images fusion. For example, the content of single-mode medical image is very single, and the fused image contains more image information, which provides a more reliable basis for diagnosis. However, in wireless communication and medical image processing, the image fusion effect is poor and the efficiency is low. To solve this problem, a image fusion algorithm based on fast finite shear wave transform and convolutional neural network is proposed for wireless communication. Firstly, the source image is decomposed by fast finite shear wave transform (FFST); secondly, the convolution neural network is improved to reduce the dimension of convolution layer from two steps, and the number of iterations and the optimal weight of convolution neural network are determined by experiments; finally, the image is fused by the inverse process of fast finite shear wave transform. The experimental results show that the algorithm has a very good effect in both objective indicators and subjective vision, and it is also very feasible in wireless communication.