Trustworthy Image Fusion with Deep Learning for Wireless Applications

Author:

Zhang Chao1,Hu Haojin1,Tai Yonghang1ORCID,Yun Lijun2ORCID,Zhang Jun1ORCID

Affiliation:

1. School of Physics and Electronic Information, Yunnan Normal University, Kunming, China

2. School of Information Science and Technology, Yunnan Normal University, Kunming, China

Abstract

To fuse infrared and visible images in wireless applications, the extraction and transmission of characteristic information security is an important task. The fused image quality depends on the effectiveness of feature extraction and the transmission of image pair characteristics. However, most fusion approaches based on deep learning do not make effective use of the features for image fusion, which results in missing semantic content in the fused image. In this paper, a novel trustworthy image fusion method is proposed to address these issues, which applies convolutional neural networks for feature extraction and blockchain technology to protect sensitive information. The new method can effectively reduce the loss of feature information by making the output of the feature extraction network in each convolutional layer to be fed to the next layer along with the production of the previous layer, and in order to ensure the similarity between the fused image and the original image, the original input image feature map is used as the input of the reconstruction network in the image reconstruction network. Compared to other methods, the experimental results show that our proposed method can achieve better quality and satisfy human perception.

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Privacy‐protected object detection through trustworthy image fusion;International Journal of Network Management;2024-05-09

2. Visible and Infrared Image Fusion Using Deep Learning;IEEE Transactions on Pattern Analysis and Machine Intelligence;2023-08

3. Image Fusion and Stylization Processing Based on Multiscale Transformation and Convolutional Neural Network;Computational Intelligence and Neuroscience;2022-04-28

4. Scene-Specialized Multitarget Detector with an SMC-PHD Filter and a YOLO Network;Computational Intelligence and Neuroscience;2022-04-28

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