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 a 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, an image fusion algorithm based on fast finite shear wave transform and convolutional neural network is proposed for wireless communication in this paper. This algorithm adopts the methods such as fast finite shear wave transform (FFST), reducing the dimension of the convolution layer, and 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.
Highlights
In the field of Space-Ground-Sea Wireless Networks, with the combination of medical image analysis, environmental detection, and digital photography with computer technology [1, 2], the development of image processing has been greatly promoted
On the basis of the existing medical fusion algorithm research, aiming at the problems of medical fusion algorithm, combined with the advantages of convolution neural network model [16], an improved medical image fusion algorithm based on fast finite shear wave transform and convolution neural network (FFST) is proposed
In order to verify the effectiveness of the algorithm, this paper selects the multi-focus image fusion (NPF) algorithm based on NSCT and pulse coupled neural network (PCNN) [25], the surface wavelet transform (SCT) [26], and the multi-focus image fusion (FGF) algorithm based on fast finite shear wave transform and guided filtering [27] for comparison
Summary
In the field of Space-Ground-Sea Wireless Networks, with the combination of medical image analysis, environmental detection, and digital photography with computer technology [1, 2], the development of image processing has been greatly promoted. Image fusion was mainly used in military applications. Lvarez et al proposed Laplace pyramid image fusion algorithm for the first time [4]. Akhtarkavan et al proposed the quantization and threshold method [5], which made image fusion enter a new stage. Image fusion [6, 7] refers to the collection of the same target image information through multi-channel, through information extraction, enhancement, denoising, or other computer technology, to collect the effective information in
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