{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:31:03Z","timestamp":1775071863257,"version":"3.50.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"27","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202416"],"award-info":[{"award-number":["62202416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62162068"],"award-info":[{"award-number":["62162068"]}],"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\/s11042-024-18141-y","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T06:05:06Z","timestamp":1706594706000},"page":"68931-68957","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A multi-weight fusion framework for infrared and visible image fusion"],"prefix":"10.1007","volume":"83","author":[{"given":"Yiqiao","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6207-9728","authenticated-orcid":false,"given":"Kangjian","family":"He","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Hongzhen","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"18141_CR1","doi-asserted-by":"publisher","first-page":"4816","DOI":"10.1109\/TIP.2020.2976190","volume":"29","author":"J Li","year":"2020","unstructured":"Li J et al (2020) DRPL: deep regression pair learning for multi-focus image fusion. IEEE Trans Image Process 29:4816\u20134831. https:\/\/doi.org\/10.1109\/TIP.2020.2976190","journal-title":"IEEE Trans Image Process"},{"key":"18141_CR2","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.inffus.2023.02.011","volume":"95","author":"H Li","year":"2023","unstructured":"Li H, Zhao J, Li J, Yu Z, Lu G (2023) Feature dynamic alignment and refinement for infrared-visible image fusion: Translation robust fusion. Inf Fusion 95:26\u201341. https:\/\/doi.org\/10.1016\/j.inffus.2023.02.011","journal-title":"Inf Fusion"},{"key":"18141_CR3","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TIP.2023.3240856","volume":"32","author":"J Li","year":"2023","unstructured":"Li J, Liang B, Lu X, Li M, Lu G, Xu Y (2023) From global to local: multi-patch and multi-scale contrastive similarity learning for unsupervised defocus blur detection. IEEE Trans Image Process 32:1158\u20131169. https:\/\/doi.org\/10.1109\/TIP.2023.3240856","journal-title":"IEEE Trans Image Process"},{"key":"18141_CR4","doi-asserted-by":"publisher","first-page":"4564","DOI":"10.1109\/JSTARS.2020.3015350","volume":"13","author":"H Zhou","year":"2020","unstructured":"Zhou H et al (2020) Feature matching for remote sensing image registration via manifold regularization. IEEE J Sel Top Appl Earth Obs Remote Sens 13:4564\u20134574. https:\/\/doi.org\/10.1109\/JSTARS.2020.3015350","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"18141_CR5","doi-asserted-by":"publisher","unstructured":"Lin X et al (2022) Learning modal-invariant and temporal-memory for video-based visible-infrared person re-identification. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18\u201324, 2022, IEEE, pp 20941\u201320950. https:\/\/doi.org\/10.1109\/CVPR52688.2022.02030","DOI":"10.1109\/CVPR52688.2022.02030"},{"key":"18141_CR6","doi-asserted-by":"publisher","unstructured":"Li S et al (2023) Logical relation inference and multiview information interaction for domain adaptation person re-identification. IEEE Trans Neural Netw Learn Syst 1\u201313. https:\/\/doi.org\/10.1109\/TNNLS.2023.3281504","DOI":"10.1109\/TNNLS.2023.3281504"},{"issue":"11","key":"18141_CR7","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1049\/ipr2.12857","volume":"17","author":"Y Zhou","year":"2023","unstructured":"Zhou Y, Xie L, He K, Xu D, Tao D, Lin X (2023) Low-light image enhancement for infrared and visible image fusion. IET Image Proc 17(11):3216\u20133234","journal-title":"IET Image Proc"},{"key":"18141_CR8","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.inffus.2019.07.005","volume":"54","author":"J Ma","year":"2020","unstructured":"Ma J et al (2020) Infrared and visible image fusion via detail preserving adversarial learning. Inf Fusion 54:85\u201398. https:\/\/doi.org\/10.1016\/j.inffus.2019.07.005","journal-title":"Inf Fusion"},{"key":"18141_CR9","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.inffus.2018.02.004","journal-title":"Inf Fusion"},{"key":"18141_CR10","doi-asserted-by":"publisher","first-page":"22511","DOI":"10.1007\/s00521-023-08916-z","volume":"35","author":"C Li","year":"2023","unstructured":"Li C et al (2023) Superpixel-based adaptive salient region analysis for infrared and visible image fusion. Neural Comput Appl 35:22511\u201322529","journal-title":"Neural Comput Appl"},{"key":"18141_CR11","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/TIP.2019.2928895","volume":"29","author":"RA Borsoi","year":"2020","unstructured":"Borsoi RA, Imbiriba T, Bermudez JCM (2020) Super-resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability. IEEE Trans Image Process 29:116\u2013127. https:\/\/doi.org\/10.1109\/TIP.2019.2928895","journal-title":"IEEE Trans Image Process"},{"key":"18141_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3237814","volume":"72","author":"J Wang","year":"2023","unstructured":"Wang J, Xi X, Li D, Li F (2023) FusionGRAM: an infrared and visible image fusion framework based on gradient residual and attention mechanism. IEEE Trans Instrum Meas 72:1\u201312. https:\/\/doi.org\/10.1109\/TIM.2023.3237814","journal-title":"IEEE Trans Instrum Meas"},{"key":"18141_CR13","doi-asserted-by":"publisher","first-page":"4733","DOI":"10.1109\/TIP.2020.2975984","volume":"29","author":"H Li","year":"2020","unstructured":"Li H, Wu X-J, Kittler J (2020) MDLatLRR: a novel decomposition method for infrared and visible image fusion. IEEE Trans Image Process 29:4733\u20134746. https:\/\/doi.org\/10.1109\/TIP.2020.2975984","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"18141_CR14","doi-asserted-by":"publisher","first-page":"8859","DOI":"10.1007\/s11042-016-3510-3","volume":"76","author":"Y Peng","year":"2017","unstructured":"Peng Y, Lu B-L (2017) Robust structured sparse representation via half-quadratic optimization for face recognition. Multim Tools Appl 76(6):8859\u20138880. https:\/\/doi.org\/10.1007\/s11042-016-3510-3","journal-title":"Multim Tools Appl"},{"issue":"1","key":"18141_CR15","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","volume":"35","author":"G Liu","year":"2013","unstructured":"Liu G, Lin Z, Yan S, Sun J, Yu Y, Ma Y (2013) Robust recovery of subspace structures by low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(1):171\u2013184. https:\/\/doi.org\/10.1109\/TPAMI.2012.88","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"18141_CR16","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1049\/iet-ipr.2014.0311","volume":"9","author":"Y Liu","year":"2015","unstructured":"Liu Y, Wang Z (2015) Simultaneous image fusion and denoising with adaptive sparse representation. IET Image Process 9(5):347\u2013357. https:\/\/doi.org\/10.1049\/iet-ipr.2014.0311","journal-title":"IET Image Process"},{"key":"18141_CR17","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.inffus.2021.02.008","volume":"71","author":"G Li","year":"2021","unstructured":"Li G, Lin Y, Qu X (2021) An infrared and visible image fusion method based on multi-scale transformation and norm optimization. Inf Fusion 71:109\u2013129. https:\/\/doi.org\/10.1016\/j.inffus.2021.02.008","journal-title":"Inf Fusion"},{"key":"18141_CR18","doi-asserted-by":"publisher","first-page":"107752","DOI":"10.1016\/j.patcog.2020.107752","volume":"113","author":"Q Zhang","year":"2021","unstructured":"Zhang Q, Wang F, Luo Y, Han J (2021) Exploring a unified low rank representation for multi-focus image fusion. Pattern Recognit 113:107752. https:\/\/doi.org\/10.1016\/j.patcog.2020.107752","journal-title":"Pattern Recognit"},{"key":"18141_CR19","doi-asserted-by":"publisher","first-page":"103839","DOI":"10.1016\/j.infrared.2021.103839","volume":"117","author":"L Ren","year":"2021","unstructured":"Ren L, Pan Z, Cao J, Liao J (2021) Infrared and visible image fusion based on variational auto-encoder and infrared feature compensation. Infrared Phys Technol 117:103839","journal-title":"Infrared Phys Technol"},{"key":"18141_CR20","doi-asserted-by":"publisher","unstructured":"Qu L, Liu S, Wang M, Song Z (2022) TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework Using Self-Supervised Multi-Task Learning. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, AAAI Press, pp 2126\u20132134. https:\/\/doi.org\/10.1609\/aaai.v36i2.20109","DOI":"10.1609\/aaai.v36i2.20109"},{"key":"18141_CR21","doi-asserted-by":"crossref","unstructured":"Zhao H, Nie R (2021) Dndt: Infrared and visible image fusion via densenet and dual-transformer. In: 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE), IEEE, pp 71\u201375","DOI":"10.1109\/ICITBE54178.2021.00025"},{"key":"18141_CR22","doi-asserted-by":"crossref","unstructured":"Qu L et al (2022) TransFuse: A Unified Transformer-based Image Fusion Framework using Self-supervised Learning. CoRR, vol. abs\/2201.07451, [Online]. Available: https:\/\/arxiv.org\/abs\/2201.07451","DOI":"10.2139\/ssrn.4130858"},{"issue":"1","key":"18141_CR23","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2022","unstructured":"Xu H, Ma J, Jiang J, Guo X, Ling H (2022) U2Fusion: a unified unsupervised image fusion network. IEEE Trans Pattern Anal Mach Intell 44(1):502\u2013518. https:\/\/doi.org\/10.1109\/TPAMI.2020.3012548","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18141_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3075747","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. https:\/\/doi.org\/10.1109\/TIM.2021.3075747","journal-title":"IEEE Trans Instrum Meas"},{"key":"18141_CR25","doi-asserted-by":"publisher","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. https:\/\/doi.org\/10.1016\/j.inffus.2018.09.004","journal-title":"Inf Fusion"},{"key":"18141_CR26","doi-asserted-by":"publisher","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, Steven Zhang X-P (2020) DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process 29:4980\u20134995. https:\/\/doi.org\/10.1109\/TIP.2020.2977573","journal-title":"IEEE Trans. Image Process"},{"key":"18141_CR27","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.inffus.2016.02.001","volume":"31","author":"J Ma","year":"2016","unstructured":"Ma J, Chen C, Li C, Huang J (2016) Infrared and visible image fusion via gradient transfer and total variation minimization. Inf Fusion 31:100\u2013109. https:\/\/doi.org\/10.1016\/j.inffus.2016.02.001","journal-title":"Inf Fusion"},{"key":"18141_CR28","doi-asserted-by":"publisher","first-page":"2761","DOI":"10.1007\/s11263-021-01501-8","volume":"129","author":"H Zhang","year":"2021","unstructured":"Zhang H, Ma J (2021) SDNet: A versatile squeeze-and-decomposition network for real-time image fusion. Int J Comput Vis 129:2761\u20132785. https:\/\/doi.org\/10.1007\/s11263-021-01501-8","journal-title":"Int J Comput Vis"},{"key":"18141_CR29","unstructured":"Ying Z, Li G, Gao W (2017) A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement. CoRR abs\/1711.00591 [Online]. Available: http:\/\/arxiv.org\/abs\/1711.00591"},{"issue":"2","key":"18141_CR30","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91\u2013110. https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int J Comput Vis"},{"issue":"5","key":"18141_CR31","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1109\/TPAMI.2010.147","volume":"33","author":"C Liu","year":"2011","unstructured":"Liu C, Yuen J, Torralba A (2011) SIFT Flow: dense Correspondence across scenes and its applications. IEEE Trans Pattern Anal Mach Intell 33(5):978\u2013994. https:\/\/doi.org\/10.1109\/TPAMI.2010.147","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18141_CR32","doi-asserted-by":"publisher","unstructured":"Zhang W, Cham W (2010) Gradient-directed composition of multi-exposure images. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, USA, 13\u201318 June 2010, IEEE Computer Society, pp 530\u2013536. https:\/\/doi.org\/10.1109\/CVPR.2010.5540168","DOI":"10.1109\/CVPR.2010.5540168"},{"issue":"3","key":"18141_CR33","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TPAMI.2018.2883553","volume":"42","author":"X Guo","year":"2020","unstructured":"Guo X, Li Y, Ma J, Ling H (2020) Mutually guided image filtering. IEEE Trans Pattern Anal Mach Intell 42(3):694\u2013707. https:\/\/doi.org\/10.1109\/TPAMI.2018.2883553","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"18141_CR34","doi-asserted-by":"publisher","first-page":"4672","DOI":"10.1109\/TIP.2012.2207396","volume":"21","author":"Z Li","year":"2012","unstructured":"Li Z, Zheng J, Rahardja S (2012) Detail-enhanced exposure fusion. IEEE Trans Image Process 21(11):4672\u20134676. https:\/\/doi.org\/10.1109\/TIP.2012.2207396","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"18141_CR35","doi-asserted-by":"publisher","first-page":"139:1","DOI":"10.1145\/2366145.2366158","volume":"31","author":"L Xu","year":"2012","unstructured":"Xu L, Yan Q, Xia Y, Jia J (2012) Structure extraction from texture via relative total variation. ACM Trans Graph 31(6):139:1-139:10. https:\/\/doi.org\/10.1145\/2366145.2366158","journal-title":"ACM Trans Graph"},{"issue":"2","key":"18141_CR36","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","volume":"26","author":"X Guo","year":"2017","unstructured":"Guo X, Li Y, Ling H (2017) LIME: low-light image enhancement via illumination map estimation. IEEE Trans Image Process 26(2):982\u2013993. https:\/\/doi.org\/10.1109\/TIP.2016.2639450","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"18141_CR37","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1109\/TPAMI.2011.146","volume":"34","author":"X Hou","year":"2012","unstructured":"Hou X, Harel J, Koch C (2012) Image signature: highlighting sparse salient regions. IEEE Trans Pattern Anal Mach Intell 34(1):194\u2013201. https:\/\/doi.org\/10.1109\/TPAMI.2011.146","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"18141_CR38","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1111\/j.1467-8659.2008.01171.x","volume":"28","author":"T Mertens","year":"2009","unstructured":"Mertens T, Kautz J, Reeth FV (2009) Exposure fusion: a simple and practical alternative to high dynamic range photography. Comput Graph Forum 28(1):161\u2013171. https:\/\/doi.org\/10.1111\/j.1467-8659.2008.01171.x","journal-title":"Comput Graph Forum"},{"key":"18141_CR39","doi-asserted-by":"publisher","first-page":"108774","DOI":"10.1016\/j.sigpro.2022.108774","volume":"202","author":"O Ulucan","year":"2023","unstructured":"Ulucan O, Ulucan D, T\u00fcrkan M (2023) Ghosting-free multi-exposure image fusion for static and dynamic scenes. Signal Process 202:108774. https:\/\/doi.org\/10.1016\/j.sigpro.2022.108774","journal-title":"Signal Process"},{"key":"18141_CR40","doi-asserted-by":"publisher","unstructured":"Bavirisetti DP, Xiao G, Liu G (2017) Multi-sensor image fusion based on fourth order partial differential equations. In: 20th International Conference on Information Fusion, FUSION 2017, Xi\u2019an, China, July 10\u201313, 2017, IEEE, pp 1\u20139. https:\/\/doi.org\/10.23919\/ICIF.2017.8009719","DOI":"10.23919\/ICIF.2017.8009719"},{"issue":"12","key":"18141_CR41","doi-asserted-by":"publisher","first-page":"5576","DOI":"10.1007\/s00034-019-01131-z","volume":"38","author":"DP Bavirisetti","year":"2019","unstructured":"Bavirisetti DP, Xiao G, Zhao J, Dhuli R, Liu G (2019) Multi-scale guided image and video fusion: a fast and efficient approach. Circuits Syst Signal Process 38(12):5576\u20135605. https:\/\/doi.org\/10.1007\/s00034-019-01131-z","journal-title":"Circuits Syst Signal Process"},{"key":"18141_CR42","doi-asserted-by":"publisher","first-page":"107734","DOI":"10.1016\/j.sigpro.2020.107734","volume":"177","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Xu S, Zhang C, Liu J, Zhang J (2020) Bayesian fusion for infrared and visible images. Signal Process 177:107734. https:\/\/doi.org\/10.1016\/j.sigpro.2020.107734","journal-title":"Signal Process"},{"key":"18141_CR43","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ins.2019.08.066","volume":"508","author":"J Chen","year":"2020","unstructured":"Chen J, Li X, Luo L, Mei X, Ma J (2020) Infrared and visible image fusion based on target-enhanced multiscale transform decomposition. Inf Sci 508:64\u201378. https:\/\/doi.org\/10.1016\/j.ins.2019.08.066","journal-title":"Inf Sci"},{"key":"18141_CR44","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.inffus.2021.02.023","volume":"73","author":"H Li","year":"2021","unstructured":"Li H, Wu X-J, Kittler J (2021) RFN-Nest: an end-to-end residual fusion network for infrared and visible images. Inf Fusion 73:72\u201386. https:\/\/doi.org\/10.1016\/j.inffus.2021.02.023","journal-title":"Inf Fusion"},{"key":"18141_CR45","doi-asserted-by":"publisher","first-page":"168914","DOI":"10.1016\/j.ijleo.2022.168914","volume":"258","author":"Y Luo","year":"2022","unstructured":"Luo Y, He K, Xu D, Yin W, Liu W (2022) Infrared and visible image fusion based on visibility enhancement and hybrid multiscale decomposition. Optik 258:168914","journal-title":"Optik"},{"key":"18141_CR46","doi-asserted-by":"publisher","unstructured":"Park S, Vien AG, Lee C (2023) Cross-modal transformers for infrared and visible image fusion. IEEE Trans Circuits Syst Video Technol 1. https:\/\/doi.org\/10.1109\/TCSVT.2023.3289170","DOI":"10.1109\/TCSVT.2023.3289170"},{"issue":"12","key":"18141_CR47","doi-asserted-by":"publisher","first-page":"9645","DOI":"10.1109\/TIM.2020.3005230","volume":"69","author":"H Li","year":"2020","unstructured":"Li H, Wu X-J, Durrani TS (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. https:\/\/doi.org\/10.1109\/TIM.2020.3005230","journal-title":"IEEE Trans Instrum Meas"},{"key":"18141_CR48","unstructured":"\u201cTNO.\u201d [Online]. Available: https:\/\/figshare.com\/articles\/TNO_Image_Fusion_Dataset\/1008029"},{"key":"18141_CR49","doi-asserted-by":"publisher","unstructured":"Zhang X, Ye P, Xiao G (2020) VIFB: A Visible and Infrared Image Fusion Benchmark. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2020, Seattle, WA, USA, June 14\u201319, 2020, Computer Vision Foundation \/ IEEE, pp 468\u2013478. https:\/\/doi.org\/10.1109\/CVPRW50498.2020.00060","DOI":"10.1109\/CVPRW50498.2020.00060"},{"key":"18141_CR50","doi-asserted-by":"publisher","unstructured":"Li C, Liang X, Lu Y, Zhao N, Tang J (2019) RGB-T object tracking: benchmark and baseline. Pattern Recognit 96:106977. https:\/\/doi.org\/10.1016\/j.patcog.2019.106977","DOI":"10.1016\/j.patcog.2019.106977"},{"key":"18141_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.2945910","volume":"2","author":"W Roberts","year":"2008","unstructured":"Roberts W, van Aardt J, Ahmed F (2008) Assessment of image fusion procedures using entropy, image quality, and multispectral classification. J Appl Remote Sens 2:1\u201328. https:\/\/doi.org\/10.1117\/1.2945910","journal-title":"J Appl Remote Sens"},{"issue":"2","key":"18141_CR52","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TIP.2005.859378","volume":"15","author":"HR Sheikh","year":"2006","unstructured":"Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430\u2013444. https:\/\/doi.org\/10.1109\/TIP.2005.859378","journal-title":"IEEE Trans Image Process"},{"key":"18141_CR53","doi-asserted-by":"publisher","unstructured":"Petrovic V, Xydeas C (2005) Objective image fusion performance characterization. In: Tenth IEEE International Conference on Computer Vision (ICCV\u201905) Volume 1, pp 1866\u20131871 Vol. 2. https:\/\/doi.org\/10.1109\/ICCV.2005.175","DOI":"10.1109\/ICCV.2005.175"},{"issue":"2","key":"18141_CR54","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.inffus.2005.10.001","volume":"8","author":"H Chen","year":"2007","unstructured":"Chen H, Varshney PK (2007) A human perception inspired quality metric for image fusion based on regional information. Inf Fusion 8(2):193\u2013207. https:\/\/doi.org\/10.1016\/j.inffus.2005.10.001","journal-title":"Inf Fusion"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18141-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18141-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18141-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T05:42:00Z","timestamp":1721886120000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18141-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,30]]},"references-count":54,"journal-issue":{"issue":"27","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["18141"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18141-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,30]]},"assertion":[{"value":"6 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 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 there is no conflict of interest regarding the publication of the article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}