{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:29:36Z","timestamp":1772166576856,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T00:00:00Z","timestamp":1627689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010909","name":"Young Scientists Fund","doi-asserted-by":"publisher","award":["61602249"],"award-info":[{"award-number":["61602249"]}],"id":[{"id":"10.13039\/501100010909","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1186\/s13634-021-00771-1","type":"journal-article","created":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T09:02:53Z","timestamp":1627722173000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Image fusion algorithm in Integrated Space-Ground-Sea Wireless Networks of B5G"],"prefix":"10.1186","volume":"2021","author":[{"given":"Xiaobing","family":"Yu","sequence":"first","affiliation":[]},{"given":"Yingliu","family":"Cui","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6521-979X","authenticated-orcid":false,"given":"Jinjin","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,31]]},"reference":[{"key":"771_CR1","doi-asserted-by":"crossref","unstructured":"N. Saeed, A. Celik, T.Y. Al-Naffouri, M.-S. Alouini, Underwater optical wireless communications, networking, and localization: A survey. Ad Hoc Netw. 94,\u00a0101935\u00a0(2019)","DOI":"10.1016\/j.adhoc.2019.101935"},{"key":"771_CR2","doi-asserted-by":"crossref","unstructured":"Y. Li et al., Medical Image Fusion Method by Deep Learning. Int. J. Cogn. Comput. Eng. 2, 21-29 (2021)","DOI":"10.1016\/j.ijcce.2020.12.004"},{"key":"771_CR3","doi-asserted-by":"crossref","unstructured":"Wang Guofen,Li Weisheng,Huang Yuping. Medical image fusion based on hybrid three-layer decomposition model and nuclear norm. Comp. Biol. Med. 2020,129:\u00a0104179 (2020).","DOI":"10.1016\/j.compbiomed.2020.104179"},{"key":"771_CR4","doi-asserted-by":"crossref","unstructured":"Lvarez, D. , P Gonz\u00e1lez-Rodr\u00edguez, and M. Kindelan . A Local Radial Basis Function Method for the Laplace\u2013Beltrami Operator. J. Sci. Comput. 86.3(2021).","DOI":"10.1007\/s10915-020-01399-3"},{"key":"771_CR5","doi-asserted-by":"crossref","unstructured":"Akhtarkavan, E. , et al. Fragile high capacity data hiding in digital images using integer-to-integer DWT and lattice vector quantization. Multimedia Tools. Appl. 79.8, 13427\u201313447 (2020).","DOI":"10.1007\/s11042-020-08662-7"},{"key":"771_CR6","doi-asserted-by":"crossref","unstructured":"Hariharan, K. , and N. R. Raajan . Performance enhanced hyperspectral and multispectral image fusion technique using ripplet type-II transform and deep neural networks for multimedia applications. Multimedia Tools Appl. 79, 1-10(2018).","DOI":"10.1007\/s11042-018-6174-3"},{"key":"771_CR7","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.apnum.2019.11.018","volume":"152","author":"DJ Liu","year":"2020","unstructured":"D.J. Liu, Z.R. Chen, The adaptive finite element method for the P-Laplace problem$1. Appl. Num. Math. 152, 323\u2013337 (2020)","journal-title":"Appl. Num. Math."},{"issue":"05","key":"771_CR8","doi-asserted-by":"publisher","first-page":"831","DOI":"10.21629\/JSEE.2019.05.02","volume":"30","author":"W Jian","year":"2019","unstructured":"W. Jian, Y. Ke, R. Ping, Q. Chunxia, Z. Xiufei, Multi-source image fusion algorithm based on fast weighted guided filter. J. Syst. Eng. Electron 30(05), 831\u2013840 (2019)","journal-title":"J. Syst. Eng. Electron"},{"issue":"7","key":"771_CR9","first-page":"37","volume":"80","author":"N Yoneda","year":"2015","unstructured":"N. Yoneda et al., Analysis of circular-to-rectangular waveguide T-junction using mode-matching technique. Electron. Commun. Japan 80(7), 37\u201346 (2015)","journal-title":"Electron. Commun. Japan"},{"key":"771_CR10","doi-asserted-by":"crossref","unstructured":"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).","DOI":"10.1080\/15623599.2018.1526630"},{"key":"771_CR11","first-page":"104","volume":"1","author":"Q Li","year":"2021","unstructured":"Q. Li et al., Medical Image Fusion Using Segment Graph Filter and Sparse Representation. Comput. Biol. Med. 1, 104\u2013239 (2021)","journal-title":"Comput. Biol. Med."},{"key":"771_CR12","doi-asserted-by":"crossref","unstructured":"Ma, C. , et al. Single image super resolution via wavelet transform fusion and SRFeat network. J. Ambient Intell. Human. Comput. 2(2020).","DOI":"10.1007\/s12652-020-02065-0"},{"key":"771_CR13","doi-asserted-by":"publisher","first-page":"107003","DOI":"10.1016\/j.ymssp.2020.107003","volume":"146","author":"F Saltari","year":"2021","unstructured":"F. Saltari, D. Dessi, F. Mastroddi, Mechanical systems virtual sensing by proportional observer and multi-resolution analysis. Mech. Syst. Signal Process 146, 107003 (2021)","journal-title":"Mech. Syst. Signal Process"},{"issue":"1","key":"771_CR14","doi-asserted-by":"publisher","first-page":"012062","DOI":"10.1088\/1755-1315\/660\/1\/012062","volume":"660","author":"P Yonghao","year":"2021","unstructured":"P. Yonghao et al., A Multi-scale Inversion Method Based on Convolutional Wavelet Transform Applied in Cross-Hole Resistivity Electrical Tomography. IOP Conf. Series Earth Environ. Sci. 660(1), 012062 (2021)","journal-title":"IOP Conf. Series Earth Environ. Sci."},{"key":"771_CR15","first-page":"1","volume":"2018","author":"X Xu","year":"2018","unstructured":"X. Xu et al., Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. J. Healthcare Eng. 2018, 1\u20138 (2018)","journal-title":"J. Healthcare Eng."},{"key":"771_CR16","doi-asserted-by":"crossref","unstructured":"Li, T. , et al. Random-Drop Data Augmentation of Deep Convolutional Neural Network for Mineral Prospectivity Mapping. Nat. Resources Res: 30, 1-12(2020).","DOI":"10.1007\/s11053-020-09742-z"},{"key":"771_CR17","doi-asserted-by":"crossref","unstructured":"Lan, R. , et al. Image denoising via deep residual convolutional neural networks. Signal Image. Video Process. 9, 1-8 (2019).","DOI":"10.1007\/s11760-019-01537-x"},{"key":"771_CR18","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.patrec.2020.01.011","volume":"131","author":"S Aich","year":"2020","unstructured":"S. Aich et al., Multi-Scale Weight Sharing Network for Image Recognition. Pattern Recogn. Lett. 131, 348\u2013354 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"771_CR19","doi-asserted-by":"crossref","unstructured":"Malekzadeh, M. . Developing new connectivity architectures for local sensing and control IoT systems. Peer-to-Peer Netw. Appl. 4, 609\u2013626 (2020).","DOI":"10.1007\/s12083-020-01019-9"},{"key":"771_CR20","doi-asserted-by":"crossref","unstructured":"H. Louati et al., Deep Convolutional Neural Network Architecture Design as a Bi-level Optimization Problem. Neurocomputing\u00a0439, 44-62 (2021)","DOI":"10.1016\/j.neucom.2021.01.094"},{"key":"771_CR21","first-page":"1","volume":"8","author":"M Varshney","year":"2021","unstructured":"M. Varshney, P. Singh, Optimizing nonlinear activation function for convolutional neural networks. Signal Image Video Process. 8, 1\u20138 (2021)","journal-title":"Signal Image Video Process."},{"key":"771_CR22","doi-asserted-by":"publisher","first-page":"164903","DOI":"10.1016\/j.ijleo.2020.164903","volume":"216","author":"S Routray","year":"2020","unstructured":"S. Routray et al., A new image denoising framework using bilateral filtering based non-subsampled shearlet transform. Optik \u2013 Int. J. Light. Electron. Optics 216, 164903 (2020)","journal-title":"Optik \u2013 Int. J. Light. Electron. Optics"},{"key":"771_CR23","first-page":"1","volume":"2016","author":"R Singh","year":"2016","unstructured":"R. Singh, A. Chakraborty, B.S. Manoj, Graph Fourier Transform based on Directed Laplacian. Int. Confer. Signal Process. Commun. IEEE 2016, 1\u20135 (2016)","journal-title":"Int. Confer. Signal Process. Commun. IEEE"},{"key":"771_CR24","doi-asserted-by":"crossref","unstructured":"M. Li, Y. Wang, Z. Wang, H. Zheng, A deep learning method based on an attention mechanism for wireless network traffic prediction. Ad Hoc Netw. 107(102258), 102258 (2020)","DOI":"10.1016\/j.adhoc.2020.102258"},{"key":"771_CR25","doi-asserted-by":"crossref","unstructured":"Panou, G. , and R. Korakitis . The direct geodesic problem and an approximate analytical solution in Cartesian coordinates on a triaxial ellipsoid. J. Appl. Geodesy 14.2, 205-213 (2020).","DOI":"10.1515\/jag-2019-0066"},{"key":"771_CR26","volume-title":"Research on image fusion method based on NSCT and PCNN [D]","author":"S Xueping","year":"2016","unstructured":"S. Xueping, Research on image fusion method based on NSCT and PCNN [D] (Tianjin University of technology, 2016)"},{"issue":"06","key":"771_CR27","first-page":"662","volume":"41","author":"T Xiaoqiang","year":"2020","unstructured":"T. Xiaoqiang, K. Lingfu, K. Deming, C. Yongqiang, Using discrete stationary wavelet transform to improve NURBS quadric surface fitting method. Acta metrologica Sinica 41(06), 662\u2013668(2020) (2020)","journal-title":"Acta metrologica Sinica"},{"key":"771_CR28","doi-asserted-by":"crossref","unstructured":"Shuaiqi Liu,Mingzhu Shi,Zhihui Zhu,Jie Zhao. Image fusion based on complex-shearlet domain with guided filtering Multidimensional Systems and Signal Processing, 28(1), 207-224 (2017).","DOI":"10.1007\/s11045-015-0343-6"},{"key":"771_CR29","doi-asserted-by":"crossref","unstructured":"Y. Zhang, An Improved Algorithm of Parameter Kernel Cutting Based on Complex Fusion Image [C]. Science and Engineering Research Center.Proceedings of 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modeling (MBDASM 2019). Sci. Eng. Res. Center, 22\u201326 (2019, 2019)","DOI":"10.2991\/mbdasm-19.2019.4"},{"key":"771_CR30","doi-asserted-by":"publisher","first-page":"107893","DOI":"10.1016\/j.comnet.2021.107893","volume":"189","author":"H Fawaz","year":"2021","unstructured":"H. Fawaz, M. El Helou, S. Lahoud, K. Khawam, A reinforcement learning approach to queue-aware scheduling in full-duplex wireless networks. Comput. Netw. 189, 107893 (2021)","journal-title":"Comput. Netw."}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-021-00771-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-021-00771-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-021-00771-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,31]],"date-time":"2021-07-31T09:17:07Z","timestamp":1627723027000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-021-00771-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,31]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["771"],"URL":"https:\/\/doi.org\/10.1186\/s13634-021-00771-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-460652\/v1","asserted-by":"object"}]},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,31]]},"assertion":[{"value":"29 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures performed in this paper were in accordance with the ethical standards of the research community. This paper does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"55"}}