Image fusion algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G
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 reliab...
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| Published in | EURASIP journal on advances in signal processing Vol. 2021; no. 1; pp. 1 - 10 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Cham
Springer International Publishing
31.07.2021
Springer Springer Nature B.V SpringerOpen |
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| ISSN | 1687-6180 1687-6172 1687-6180 |
| DOI | 10.1186/s13634-021-00771-1 |
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| 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. |
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| AbstractList | 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. 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. |
| ArticleNumber | 55 |
| Audience | Academic |
| Author | Yu, Xiaobing Cui, Yingliu Wang, Xin Zhang, Jinjin |
| Author_xml | – sequence: 1 givenname: Xiaobing surname: Yu fullname: Yu, Xiaobing email: cosine@nau.edu.cn organization: School of Information Engineering, Nanjing Audit University – sequence: 2 givenname: Yingliu surname: Cui fullname: Cui, Yingliu organization: School of Information Engineering, Nanjing Audit University – sequence: 3 givenname: Xin surname: Wang fullname: Wang, Xin organization: School of Information Engineering, Nanjing Audit University – sequence: 4 givenname: Jinjin orcidid: 0000-0001-6521-979X surname: Zhang fullname: Zhang, Jinjin organization: School of Information Engineering, Nanjing Audit University |
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| Cites_doi | 10.1016/j.ijleo.2020.164903 10.1007/s10915-020-01399-3 10.1016/j.ymssp.2020.107003 10.1016/j.ijcce.2020.12.004 10.1007/s11045-015-0343-6 10.1007/s11053-020-09742-z 10.1016/j.patrec.2020.01.011 10.1007/s11042-020-08662-7 10.1007/s12652-020-02065-0 10.1088/1755-1315/660/1/012062 10.1016/j.adhoc.2020.102258 10.1007/s11760-019-01537-x 10.1016/j.compbiomed.2020.104179 10.1007/s12083-020-01019-9 10.1016/j.adhoc.2019.101935 10.21629/JSEE.2019.05.02 10.2991/mbdasm-19.2019.4 10.1007/s11042-018-6174-3 10.1016/j.comnet.2021.107893 10.1016/j.neucom.2021.01.094 10.1016/j.apnum.2019.11.018 10.1080/15623599.2018.1526630 10.1515/jag-2019-0066 |
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| Keywords | Space-Ground-Sea Wireless Networks Image recognition Neural network Fast finite shear wave transform Image fusion |
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Optics202021616490310.1016/j.ijleo.2020.164903 – reference: Wang Guofen,Li Weisheng,Huang Yuping. Medical image fusion based on hybrid three-layer decomposition model and nuclear norm. Comp. Biol. Med. 2020,129: 104179 (2020). – reference: YonghaoPA Multi-scale Inversion Method Based on Convolutional Wavelet Transform Applied in Cross-Hole Resistivity Electrical TomographyIOP Conf. Series Earth Environ. Sci.2021660101206210.1088/1755-1315/660/1/012062 – reference: Li, T. , et al. Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping. Nat. Resources Res: 30, 1-12(2020). – reference: SaltariFDessiDMastroddiFMechanical systems virtual sensing by proportional observer and multi-resolution analysisMech. Syst. Signal Process202114610700310.1016/j.ymssp.2020.107003 – reference: XuXAtrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural NetworksJ. Healthcare Eng.2018201818 – reference: AichSMulti-Scale Weight Sharing Network for Image RecognitionPattern Recogn. Lett.202013134835410.1016/j.patrec.2020.01.011 – reference: Luong, D. L. , D. H. Tran , and P. T. Nguyen. Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution. Int. J. Construct. Manage. 21(3) 1-13(2018). – reference: VarshneyMSinghPOptimizing nonlinear activation function for convolutional neural networksSignal Image Video Process.2021818 – reference: LiuDJChenZRThe adaptive finite element method for the P-Laplace problem$1Appl. Num. Math.2020152323337406738110.1016/j.apnum.2019.11.018 – reference: YonedaNAnalysis of circular-to-rectangular waveguide T-junction using mode-matching techniqueElectron. Commun. Japan20158073746 – reference: N. Saeed, A. Celik, T.Y. Al-Naffouri, M.-S. Alouini, Underwater optical wireless communications, networking, and localization: A survey. 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IEEE – volume-title: Research on image fusion method based on NSCT and PCNN [D] year: 2016 ident: 771_CR26 – ident: 771_CR3 doi: 10.1016/j.compbiomed.2020.104179 – volume: 1 start-page: 104 year: 2021 ident: 771_CR11 publication-title: Comput. Biol. Med. – ident: 771_CR19 doi: 10.1007/s12083-020-01019-9 – ident: 771_CR1 doi: 10.1016/j.adhoc.2019.101935 – volume: 30 start-page: 831 issue: 05 year: 2019 ident: 771_CR8 publication-title: J. Syst. Eng. Electron doi: 10.21629/JSEE.2019.05.02 – ident: 771_CR29 doi: 10.2991/mbdasm-19.2019.4 – ident: 771_CR6 doi: 10.1007/s11042-018-6174-3 – volume: 189 start-page: 107893 year: 2021 ident: 771_CR30 publication-title: Comput. Netw. doi: 10.1016/j.comnet.2021.107893 – ident: 771_CR20 doi: 10.1016/j.neucom.2021.01.094 – volume: 152 start-page: 323 year: 2020 ident: 771_CR7 publication-title: Appl. Num. 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| Snippet | In recent years, in Space-Ground-Sea Wireless Networks, the rapid development of image recognition also promotes the development of images fusion. For example,... Abstract In recent years, in Space-Ground-Sea Wireless Networks, the rapid development of image recognition also promotes the development of images fusion. For... |
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| SubjectTerms | Algorithms Artificial neural networks Computer vision Engineering Fast finite shear wave transform Image fusion Image processing Image recognition Integrated Space-Ground-Sea Wireless Networks for B5G Medical imaging Medical imaging equipment Neural network Neural networks Object recognition Quantum Information Technology Shear Signal,Image and Speech Processing Space-Ground-Sea Wireless Networks Spintronics Telecommunication systems Wireless communications Wireless networks |
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| Title | Image fusion algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G |
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