{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T09:32:51Z","timestamp":1763458371981,"version":"3.37.3"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"42","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515110031"],"award-info":[{"award-number":["2021A1515110031"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2021YFB2900903"],"award-info":[{"award-number":["2021YFB2900903"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62075183","62302105"],"award-info":[{"award-number":["62075183","62302105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-19659-x","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T09:04:47Z","timestamp":1719911087000},"page":"90393-90419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Saliency guided progressive fusion of infrared and polarization for military images with complex backgrounds$$^{\\star }$$"],"prefix":"10.1007","volume":"83","author":[{"given":"Yukai","family":"Lao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5507-4985","authenticated-orcid":false,"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiazhen","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianglei","family":"Di","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"19659_CR1","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.inffus.2020.05.002","volume":"63","author":"X Zhang","year":"2020","unstructured":"Zhang X, Ye P, Leung H, Gong K, Xiao G (2020) Object fusion tracking based on visible and infrared images: A comprehensive review. Inf Fusion 63:166\u2013187","journal-title":"Inf Fusion"},{"key":"19659_CR2","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.measurement.2017.06.032","volume":"110","author":"T Qiao","year":"2017","unstructured":"Qiao T, Chen L, Pang Y, Yan G, Miao C (2017) Integrative binocular vision detection method based on infrared and visible light fusion for conveyor belts longitudinal tear. Meas 110:192\u2013201","journal-title":"Meas"},{"key":"19659_CR3","doi-asserted-by":"crossref","unstructured":"Kumar P, Mittal A, Kumar P (2006) Fusion of thermal infrared and visible spectrum video for robust surveillance. In: Vision computer graphics and image processing: 5th Indian Conference, ICVGIP 2006, Madurai, India, December 13\u201316, 2006. Springer, Proceedings, pp 528\u2013539","DOI":"10.1007\/11949619_47"},{"key":"19659_CR4","doi-asserted-by":"crossref","first-page":"4980","DOI":"10.1109\/TIP.2020.2977573","volume":"29","author":"J Ma","year":"2020","unstructured":"Ma J, Xu H, Jiang J, Mei X, Zhang X-P (2020) DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans Image Process 29:4980\u20134995","journal-title":"IEEE Trans Image Process"},{"issue":"8","key":"19659_CR5","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.cviu.2009.03.002","volume":"113","author":"Y-Q Zhao","year":"2009","unstructured":"Zhao Y-Q, Zhang L, Zhang D, Pan Q (2009) Object separation by polarimetric and spectral imagery fusion. Comput Vis Image Underst 113(8):855\u2013866","journal-title":"Comput Vis Image Underst"},{"key":"19659_CR6","doi-asserted-by":"crossref","unstructured":"Karim S, Tong G, Li J, Qadir A, Farooq U, Yu Y (2022) Current advances and future perspectives of image fusion: A comprehensive review, Information Fusion","DOI":"10.1016\/j.inffus.2022.09.019"},{"key":"19659_CR7","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1109\/TCI.2022.3228633","volume":"8","author":"H Xu","year":"2022","unstructured":"Xu H, Sun Y, Mei X, Tian X, Ma J (2022) Attention-guided polarization image fusion using salient information distribution. IEEE Trans Comput Imag 8:1117\u20131130","journal-title":"IEEE Trans Comput Imag"},{"issue":"6","key":"19659_CR8","first-page":"803","volume":"6","author":"S Li","year":"2013","unstructured":"Li S, Jiang H, Zhu J, Duan J, Fu Q, Fu Y-G, Dong K-Y (2013) Development status and key technologies of polarization imaging detection. Chinese Opt 6(6):803\u2013809","journal-title":"Chinese Opt"},{"issue":"24","key":"19659_CR9","doi-asserted-by":"crossref","first-page":"43601","DOI":"10.1364\/OE.472214","volume":"30","author":"J Liu","year":"2022","unstructured":"Liu J, Duan J, Hao Y, Chen G, Zhang H (2022) Semantic-guided polarization image fusion method based on a dual-discriminator gan. Opt Express 30(24):43601\u201343621","journal-title":"Opt Express"},{"key":"19659_CR10","doi-asserted-by":"crossref","first-page":"108045","DOI":"10.1016\/j.patcog.2021.108045","volume":"118","author":"J Zhang","year":"2021","unstructured":"Zhang J, Shao J, Chen J, Yang D, Liang B (2021) Polarization image fusion with self-learned fusion strategy. Pattern Recogn 118:108045","journal-title":"Pattern Recogn"},{"issue":"2","key":"19659_CR11","doi-asserted-by":"crossref","first-page":"023021","DOI":"10.1117\/1.JEI.25.2.023021","volume":"27","author":"J Zhang","year":"2018","unstructured":"Zhang J, Zhang Y, Shi Z (2018) Long-wave infrared polarization feature extraction and image fusion based on the orthogonality difference method. J Electron Imaging 27(2):023021\u2013023021","journal-title":"J Electron Imaging"},{"key":"19659_CR12","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.inffus.2021.06.008","volume":"76","author":"H Zhang","year":"2021","unstructured":"Zhang H, Xu H, Tian X, Jiang J, Ma J (2021) Image fusion meets deep learning: A survey and perspective. Inf Fusion 76:323\u2013336","journal-title":"Inf Fusion"},{"key":"19659_CR13","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.inffus.2018.02.004","volume":"45","author":"J Ma","year":"2019","unstructured":"Ma J, Ma Y, Li C (2019) Infrared and visible image fusion methods and applications: A survey. Inf fusion 45:153\u2013178","journal-title":"Inf fusion"},{"issue":"2","key":"19659_CR14","first-page":"57","volume":"28","author":"L Zhan","year":"2017","unstructured":"Zhan L, Zhuang Y, Huang L (2017) Infrared and visible images fusion method based on discrete wavelet transform. J Comput 28(2):57\u201371","journal-title":"J Comput"},{"key":"19659_CR15","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.infrared.2016.05.012","volume":"77","author":"Z Fu","year":"2016","unstructured":"Fu Z, Wang X, Xu J, Zhou N, Zhao Y (2016) Infrared and visible images fusion based on rpca and nsct. Infrared Phys Technol 77:114\u2013123","journal-title":"Infrared Phys Technol"},{"key":"19659_CR16","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.05.004","volume":"33","author":"S Li","year":"2017","unstructured":"Li S, Kang X, Fang L, Hu J, Yin H (2017) Pixel-level image fusion: A survey of the state of the art. Inf Fusion 33:100\u2013112","journal-title":"Inf Fusion"},{"issue":"5","key":"19659_CR17","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2018","unstructured":"Li H, Wu X-J (2018) DenseFuse: A fusion approach to infrared and visible images. IEEE Trans Image Process 28(5):2614\u20132623","journal-title":"IEEE Trans Image Process"},{"key":"19659_CR18","first-page":"1","volume":"71","author":"Z Wang","year":"2022","unstructured":"Wang Z, Chen Y, Shao W, Li H, Zhang L (2022) SwinFuse: A residual swin transformer fusion network for infrared and visible images. IEEE Trans Instrum Meas 71:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"19659_CR19","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.inffus.2018.09.004","volume":"48","author":"J Ma","year":"2019","unstructured":"Ma J, Yu W, Liang P, Li C, Jiang J (2019) FusionGAN: A generative adversarial network for infrared and visible image fusion. Inf Fusion 48:11\u201326","journal-title":"Inf Fusion"},{"key":"19659_CR20","first-page":"1","volume":"70","author":"J Ma","year":"2020","unstructured":"Ma J, Zhang H, Shao Z, Liang P, Xu H (2020) GANMcC: A generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Trans Instrum Meas 70:1\u201314","journal-title":"IEEE Trans Instrum Meas"},{"key":"19659_CR21","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.inffus.2021.02.019","volume":"72","author":"Y Fu","year":"2021","unstructured":"Fu Y, Wu X-J, Durrani T (2021) Image fusion based on generative adversarial network consistent with perception. Inf Fusion 72:110\u2013125","journal-title":"Inf Fusion"},{"key":"19659_CR22","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2019.07.011","volume":"54","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Liu Y, Sun P, Yan H, Zhao X, Zhang L (2020) IFCNN: A general image fusion framework based on convolutional neural network. Inf Fusion 54:99\u2013118","journal-title":"Inf Fusion"},{"key":"19659_CR23","doi-asserted-by":"crossref","unstructured":"Zhang H, Xu H, Xiao Y, Guo X, Ma J (2020) Rethinking the image fusion: A fast unified image fusion network based on proportional maintenance of gradient and intensity. In: Proceedings of the AAAI conference on artificial intelligence, Vol.\u00a034. pp 12797\u201312804","DOI":"10.1609\/aaai.v34i07.6975"},{"key":"19659_CR24","doi-asserted-by":"crossref","first-page":"2761","DOI":"10.1007\/s11263-021-01501-8","volume":"129","author":"H Zhang","year":"2021","unstructured":"Zhang H, Ma J (2021) SDfusion: A versatile squeeze-and-decomposition network for real-time image fusion. Int J Comput Vision 129:2761\u20132785","journal-title":"Int J Comput Vision"},{"key":"19659_CR25","first-page":"1","volume":"70","author":"J Ma","year":"2021","unstructured":"Ma J, Tang L, Xu M, Zhang H, Xiao G (2021) STDfusionnet: An infrared and visible image fusion network based on salient target detection. IEEE Trans Instrum Meas 70:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"19659_CR26","doi-asserted-by":"crossref","unstructured":"Wang Z, Shao W, Chen Y, Xu J, Zhang X (2022) Infrared and visible image fusion via interactive compensatory attention adversarial learning. IEEE Trans Multimedia","DOI":"10.1109\/TMM.2022.3228685"},{"key":"19659_CR27","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.ins.2017.09.010","volume":"432","author":"Z Zhu","year":"2018","unstructured":"Zhu Z, Yin H, Chai Y, Li Y, Qi G (2018) A novel multi-modality image fusion method based on image decomposition and sparse representation. Inf Sci 432:516\u2013529","journal-title":"Inf Sci"},{"key":"19659_CR28","first-page":"20","volume":"7","author":"J Rocca","year":"2019","unstructured":"Rocca J (2019) Understanding generative adversarial networks (gans). Medium 7:20","journal-title":"Medium"},{"issue":"16","key":"19659_CR29","doi-asserted-by":"crossref","first-page":"4255","DOI":"10.1364\/OL.466191","volume":"47","author":"K Li","year":"2022","unstructured":"Li K, Qi M, Zhuang S, Yang Y, Gao J (2022) TIPFNet: a transformer-based infrared polarization image fusion network. Opt Lett 47(16):4255\u20134258","journal-title":"Opt Lett"},{"key":"19659_CR30","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.infrared.2013.05.008","volume":"60","author":"F Yang","year":"2013","unstructured":"Yang F, Wei H (2013) Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules. Infrared Phys Technol 60:235\u2013243","journal-title":"Infrared Phys Technol"},{"key":"19659_CR31","doi-asserted-by":"crossref","unstructured":"Li X, Huang Q (2017) Target detection for infrared polarization image in the background of desert. In: 2017 IEEE 9th International conference on communication software and networks (ICCSN). IEEE, pp 1147\u20131151","DOI":"10.1109\/ICCSN.2017.8230290"},{"issue":"22","key":"19659_CR32","doi-asserted-by":"crossref","first-page":"5453","DOI":"10.1364\/AO.45.005453","volume":"45","author":"JS Tyo","year":"2006","unstructured":"Tyo JS, Goldstein DL, Chenault DB, Shaw JA (2006) Review of passive imaging polarimetry for remote sensing applications. Appl Opt 45(22):5453\u20135469","journal-title":"Appl Opt"},{"issue":"1","key":"19659_CR33","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s00530-021-00802-9","volume":"28","author":"J Zhang","year":"2022","unstructured":"Zhang J, Zhang X, Li T, Zeng Y, Lv G, Nian F (2022) Visible light polarization image desmogging via cycle convolutional neural network. Multimedia Syst 28(1):45\u201355","journal-title":"Multimedia Syst"},{"issue":"9","key":"19659_CR34","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1364\/AO.20.001537","volume":"20","author":"JE Solomon","year":"1981","unstructured":"Solomon JE (1981) Polarization imaging. Appl Opt 20(9):1537\u20131544","journal-title":"Appl Opt"},{"key":"19659_CR35","unstructured":"Zhou H, Wu W, Zhang Y, Ma J, Ling H (2021) Semantic-supervised infrared and visible image fusion via a dual-discriminator generative adversarial network. IEEE Trans Multimedia"},{"key":"19659_CR36","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/TMM.2020.2997127","volume":"23","author":"J Li","year":"2020","unstructured":"Li J, Huo H, Li C, Wang R, Feng Q (2020) AttentionFGAN: Infrared and visible image fusion using attention-based generative adversarial networks. IEEE Trans Multimedia 23:1383\u20131396","journal-title":"IEEE Trans Multimedia"},{"issue":"12","key":"19659_CR37","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","volume":"69","author":"H Li","year":"2020","unstructured":"Li H, Wu X-J, Durrani T (2020) Nestfuse: An infrared and visible image fusion architecture based on nest connection and spatial\/channel attention models. IEEE Trans Instrum Meas 69(12):9645\u20139656","journal-title":"IEEE Trans Instrum Meas"},{"key":"19659_CR38","first-page":"1","volume":"71","author":"Z Wang","year":"2022","unstructured":"Wang Z, Wu Y, Wang J, Xu J, Shao W (2022) Res2fusion: Infrared and visible image fusion based on dense res2net and double nonlocal attention models. IEEE Trans Instrum Meas 71:1\u201312","journal-title":"IEEE Trans Instrum Meas"},{"key":"19659_CR39","first-page":"1","volume":"71","author":"X Wang","year":"2022","unstructured":"Wang X, Hua Z, Li J (2022) Paccdu: Pyramid attention cross-convolutional dual unet for infrared and visible image fusion. IEEE Trans Instrum Meas 71:1\u201316","journal-title":"IEEE Trans Instrum Meas"},{"key":"19659_CR40","first-page":"1","volume":"71","author":"X Li","year":"2022","unstructured":"Li X, Chen H, Li Y, Peng Y (2022) Mafusion: Multiscale attention network for infrared and visible image fusion. IEEE Trans Instrum Meas 71:1\u201316","journal-title":"IEEE Trans Instrum Meas"},{"key":"19659_CR41","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.inffus.2022.03.007","volume":"83","author":"L Tang","year":"2022","unstructured":"Tang L, Yuan J, Zhang H, Jiang X, Ma J (2022) PIAFusion: A progressive infrared and visible image fusion network based on illumination aware. Inf Fusion 83:79\u201392","journal-title":"Inf Fusion"},{"key":"19659_CR42","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","volume":"82","author":"L Tang","year":"2022","unstructured":"Tang L, Yuan J, Ma J (2022) Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network. Inf Fusion 82:28\u201342","journal-title":"Inf Fusion"},{"issue":"7","key":"19659_CR43","first-page":"1","volume":"38","author":"G Qu","year":"2002","unstructured":"Qu G, Zhang D, Yan P (2002) Information measure for performance of image fusion. Electron Lett 38(7):1","journal-title":"Electron Lett"},{"issue":"4","key":"19659_CR44","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1088\/0957-0233\/8\/4\/002","volume":"8","author":"Y-J Rao","year":"1997","unstructured":"Rao Y-J (1997) In-fibre bragg grating sensors. Meas Sci Technol 8(4):355","journal-title":"Meas Sci Technol"},{"issue":"1","key":"19659_CR45","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/TPAMI.2011.109","volume":"34","author":"Z Liu","year":"2011","unstructured":"Liu Z, Blasch E, Xue Z, Zhao J, Laganiere R, Wu W (2011) Objective assessment of multiresolution image fusion algorithms for context enhancement in night vision: a comparative study. IEEE Trans Pattern Anal Mach Intell 34(1):94\u2013109","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"19659_CR46","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2011.08.002","volume":"14","author":"Y Han","year":"2013","unstructured":"Han Y, Cai Y, Cao Y, Xu X (2013) A new image fusion performance metric based on visual information fidelity. Inf Fusion 14(2):127\u2013135","journal-title":"Inf Fusion"},{"issue":"4","key":"19659_CR47","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1088\/0957-0233\/8\/4\/002","volume":"8","author":"Y-J Rao","year":"1997","unstructured":"Rao Y-J (1997) In-fibre bragg grating sensors. Meas Sci Technol 8(4):355","journal-title":"Meas Sci Technol"},{"issue":"2","key":"19659_CR48","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.inffus.2006.02.001","volume":"8","author":"F Nencini","year":"2007","unstructured":"Nencini F, Garzelli A, Baronti S, Alparone L (2007) Remote sensing image fusion using the curvelet transform. Inf fusion 8(2):143\u2013156","journal-title":"Inf fusion"},{"issue":"2","key":"19659_CR49","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.inffus.2005.09.006","volume":"8","author":"JJ Lewis","year":"2007","unstructured":"Lewis JJ, O\u2019Callaghan RJ, Nikolov SG, Bull DR, Canagarajah N (2007) Pixel-and region-based image fusion with complex wavelets. Inf Fusion 8(2):119\u2013130","journal-title":"Inf Fusion"},{"key":"19659_CR50","doi-asserted-by":"crossref","unstructured":"Chipman LJ, Orr TM, Graham LN (1995) Wavelets and image fusion. In: Proceedings., International Conference on Image Processing, Vol.\u00a03. IEEE, pp 248\u2013251","DOI":"10.1109\/ICIP.1995.537627"},{"key":"19659_CR51","doi-asserted-by":"crossref","unstructured":"Burt PJ, Adelson EH (1987) The laplacian pyramid as a compact image code. In: Readings in computer vision. Elsevier, pp 671\u2013679","DOI":"10.1016\/B978-0-08-051581-6.50065-9"},{"issue":"10","key":"19659_CR52","doi-asserted-by":"crossref","first-page":"3089","DOI":"10.1109\/TIP.2006.877507","volume":"15","author":"AL Da Cunha","year":"2006","unstructured":"Da Cunha AL, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089\u20133101","journal-title":"IEEE Trans Image Process"},{"key":"19659_CR53","doi-asserted-by":"crossref","unstructured":"Zhao Z, Bai H, Zhu Y, Zhang J, Xu S, Zhang Y, Zhang K, Meng D, Timofte R, Van\u00a0Gool L (2023) Ddfm: denoising diffusion model for multi-modality image fusion. arXiv:2303.06840","DOI":"10.1109\/ICCV51070.2023.00742"},{"key":"19659_CR54","doi-asserted-by":"crossref","unstructured":"Zhao Z, Bai H, Zhang J, Zhang Y, Xu S, Lin Z, Timofte R, Van\u00a0Gool L (2023) Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 5906\u20135916","DOI":"10.1109\/CVPR52729.2023.00572"},{"key":"19659_CR55","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"19659_CR56","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/JAS.2022.105686","volume":"9","author":"J Ma","year":"2022","unstructured":"Ma J, Tang L, Fan F, Huang J, Mei X, Ma Y (2022) SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer. IEEE\/CAA J Autom Sinica 9(7):1200\u20131217","journal-title":"IEEE\/CAA J Autom Sinica"},{"key":"19659_CR57","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neucom.2022.04.087","volume":"494","author":"DM Jim\u00e9nez-Bravo","year":"2022","unstructured":"Jim\u00e9nez-Bravo DM, Murciego \u00c1L, Mendes AS, San Bl\u00e1s HS, Bajo J (2022) Multi-object tracking in traffic environments: A systematic literature review. Neurocomput 494:43\u201355","journal-title":"Neurocomput"},{"key":"19659_CR58","doi-asserted-by":"publisher","unstructured":"Li Q, Han G, Liu P, Yang H, Wu J, Liu D (2021) An infrared and visible image fusion method guided by saliency and gradient information. IEEE Access 9:108942\u2013108958. https:\/\/doi.org\/10.1109\/ACCESS.2021.3101639","DOI":"10.1109\/ACCESS.2021.3101639"},{"key":"19659_CR59","doi-asserted-by":"publisher","unstructured":"Qingwei Z, Fangfang F, Yiwen Z, Changying W, Zhongjie X (2022) An implicit salienct guided infrared and visible image fusion method. In: 2022 IEEE 22nd International conference on communication technology (ICCT). pp 1612\u20131616. https:\/\/doi.org\/10.1109\/ICCT56141.2022.10073368","DOI":"10.1109\/ICCT56141.2022.10073368"},{"key":"19659_CR60","doi-asserted-by":"publisher","unstructured":"Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE\/ACM 26th International symposium on quality of service (IWQoS). pp 1\u20132. https:\/\/doi.org\/10.1109\/IWQoS.2018.8624183","DOI":"10.1109\/IWQoS.2018.8624183"},{"key":"19659_CR61","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980"},{"issue":"12","key":"19659_CR62","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.1016\/S1874-1029(08)60174-3","volume":"34","author":"Q Xiao-Bo","year":"2008","unstructured":"Xiao-Bo Q, Jing-Wen Y, Hong-Zhi X, Zi-Qian Z (2008) Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Autom Sinica 34(12):1508\u20131514","journal-title":"Acta Autom Sinica"},{"key":"19659_CR63","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.infrared.2017.04.018","volume":"83","author":"C Liu","year":"2017","unstructured":"Liu C, Qi Y, Ding W (2017) Infrared and visible image fusion method based on saliency detection in sparse domain. Infrared Phys Technol 83:94\u2013102","journal-title":"Infrared Phys Technol"},{"key":"19659_CR64","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.infrared.2015.07.003","volume":"72","author":"W Gan","year":"2015","unstructured":"Gan W, Wu X, Wu W, Yang X, Ren C, He X, Liu K (2015) Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter. Infrared Phys Technol 72:37\u201351","journal-title":"Infrared Phys Technol"},{"key":"19659_CR65","doi-asserted-by":"crossref","first-page":"101828","DOI":"10.1016\/j.inffus.2023.101828","volume":"98","author":"D Wang","year":"2023","unstructured":"Wang D, Liu J, Liu R, Fan X (2023) An interactively reinforced paradigm for joint infrared-visible image fusion and saliency object detection. Inf Fusion 98:101828","journal-title":"Inf Fusion"},{"key":"19659_CR66","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.inffus.2022.09.030","volume":"91","author":"J Liu","year":"2023","unstructured":"Liu J, Dian R, Li S, Liu H (2023) Sgfusion: A saliency guided deep-learning framework for pixel-level image fusion. Inf Fusion 91:205\u2013214","journal-title":"Inf Fusion"},{"key":"19659_CR67","doi-asserted-by":"crossref","unstructured":"Tang L, Zhang H, Xu H, Ma J (2023) Rethinking the necessity of image fusion in high-level vision tasks: A practical infrared and visible image fusion network based on progressive semantic injection and scene fidelity. Inf Fusion 101870","DOI":"10.1016\/j.inffus.2023.101870"},{"key":"19659_CR68","doi-asserted-by":"crossref","first-page":"7790","DOI":"10.1109\/TIP.2021.3109518","volume":"30","author":"W Zhou","year":"2021","unstructured":"Zhou W, Liu J, Lei J, Yu L, Hwang J-N (2021) Gmnet: Graded-feature multilabel-learning network for rgb-thermal urban scene semantic segmentation. IEEE Trans Image Process 30:7790\u20137802","journal-title":"IEEE Trans Image Process"},{"key":"19659_CR69","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.infrared.2015.07.003","volume":"72","author":"W Gan","year":"2015","unstructured":"Gan W, Wu X, Wu W, Yang X, Ren C, He X, Liu K (2015) Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter. Infrared Phys Technol 72:37\u201351","journal-title":"Infrared Phys Technol"},{"key":"19659_CR70","doi-asserted-by":"crossref","unstructured":"Tang W, He F, Liu Y (2022) Ydtr: Infrared and visible image fusion via y-shape dynamic transformer. IEEE Trans Multimedia","DOI":"10.1109\/TMM.2022.3192661"},{"key":"19659_CR71","doi-asserted-by":"crossref","unstructured":"Vs V, Valanarasu JMJ, Oza P, Patel VM (2022) Image fusion transformer. In: 2022 IEEE International conference on image processing (ICIP). IEEE, pp 3566\u20133570","DOI":"10.1109\/ICIP46576.2022.9897280"},{"issue":"12","key":"19659_CR72","doi-asserted-by":"crossref","first-page":"2121","DOI":"10.1109\/JAS.2022.106082","volume":"9","author":"L Tang","year":"2022","unstructured":"Tang L, Deng Y, Ma Y, Huang J, Ma J (2022) Superfusion: A versatile image registration and fusion network with semantic awareness. IEEE\/CAA J Autom Sinica 9(12):2121\u20132137","journal-title":"IEEE\/CAA J Autom Sinica"},{"issue":"4","key":"19659_CR73","doi-asserted-by":"crossref","first-page":"4802","DOI":"10.1364\/OE.416130","volume":"29","author":"K Xiang","year":"2021","unstructured":"Xiang K, Yang K, Wang K (2021) Polarization-driven semantic segmentation via efficient attention-bridged fusion. Opt Express 29(4):4802\u20134820","journal-title":"Opt Express"},{"key":"19659_CR74","unstructured":"Liu Z, Wang B, Wang L, Mao C, Li Y (2023) Sharecmp: Polarization-aware rgb-p semantic segmentation. arXiv:2312.03430"},{"key":"19659_CR75","unstructured":"El-Sayed MA, Hafeez TA-E (2012) New edge detection technique based on the shannon entropy in gray level images. arXiv:1211.2502"},{"issue":"3","key":"19659_CR76","first-page":"25","volume":"13","author":"ME Taha","year":"2023","unstructured":"Taha ME, Mostafa T, El-Rahman A, Abd El-Hafeez T (2023) A novel hybrid approach to masked face recognition using robust pca and goa optimizer. Sci J Damietta Fac Sci 13(3):25\u201335","journal-title":"Sci J Damietta Fac Sci"},{"issue":"15","key":"19659_CR77","doi-asserted-by":"crossref","first-page":"6727","DOI":"10.3390\/s23156727","volume":"23","author":"M Eman","year":"2023","unstructured":"Eman M, Mahmoud TM, Ibrahim MM, Abd El-Hafeez T (2023) Innovative hybrid approach for masked face recognition using pretrained mask detection and segmentation, robust pca, and knn classifier. Sens 23(15):6727","journal-title":"Sens"},{"issue":"4","key":"19659_CR78","first-page":"1","volume":"2","author":"AA Ali","year":"2019","unstructured":"Ali AA, El-Hafeez T, Mohany Y (2019) A robust and efficient system to detect human faces based on facial features. Asian J Res Comput Sci 2(4):1\u201312","journal-title":"Asian J Res Comput Sci"},{"issue":"20","key":"19659_CR79","doi-asserted-by":"crossref","first-page":"35596","DOI":"10.1364\/OE.465214","volume":"30","author":"W Jiang","year":"2022","unstructured":"Jiang W, Wu J, Chen C, Chen J, Zeng X, Zhong L, Di J, Wu X, Qin Y (2022) Registration of multi-modal images under a complex background combining multiscale features extraction and semantic segmentation. Opt Express 30(20):35596\u201335607","journal-title":"Opt Express"},{"key":"19659_CR80","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.dib.2017.09.038","volume":"15","author":"A Toet","year":"2017","unstructured":"Toet A (2017) The tno multiband image data collection. Data Brief 15:249\u2013251","journal-title":"Data Brief"},{"key":"19659_CR81","doi-asserted-by":"crossref","first-page":"108042","DOI":"10.1016\/j.optlaseng.2024.108042","volume":"176","author":"J Wang","year":"2024","unstructured":"Wang J, Jiang M, Kong J (2024) Mdan: Multilevel dual-branch attention network for infrared and visible image fusion. Opt Lasers Eng 176:108042","journal-title":"Opt Lasers Eng"},{"key":"19659_CR82","doi-asserted-by":"crossref","first-page":"102147","DOI":"10.1016\/j.inffus.2023.102147","volume":"103","author":"H Li","year":"2024","unstructured":"Li H, Wu X-J (2024) Crossfuse: A novel cross attention mechanism based infrared and visible image fusion approach. Inf Fusion 103:102147","journal-title":"Inf Fusion"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19659-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19659-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19659-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T20:07:58Z","timestamp":1735416478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19659-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,2]]},"references-count":82,"journal-issue":{"issue":"42","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["19659"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19659-x","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,7,2]]},"assertion":[{"value":"9 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 June 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interests"}}]}}