{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T14:52:03Z","timestamp":1778597523454,"version":"3.51.4"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"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":["Grant No. 62003065"],"award-info":[{"award-number":["Grant No. 62003065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Project of Chongqing Municipal Education Commission","award":["Grant No. KJQN202200564"],"award-info":[{"award-number":["Grant No. KJQN202200564"]}]},{"name":"Science and Technology Research Project of Chongqing Municipal Education Commission","award":["Grant No. KJZD202200504"],"award-info":[{"award-number":["Grant No. KJZD202200504"]}]},{"name":"Fund project of Chongqing Normal University","award":["Grant No. 21XLB032"],"award-info":[{"award-number":["Grant No. 21XLB032"]}]},{"name":"Chongqing Education Science 14th Five Year Plan Project","award":["Grant No. 2022-576"],"award-info":[{"award-number":["Grant No. 2022-576"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s10489-024-06039-z","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T04:31:14Z","timestamp":1733805074000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A multi-focus image fusion network with local-global joint attention module"],"prefix":"10.1007","volume":"55","author":[{"given":"Xinheng","family":"Zou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiping","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"issue":"9","key":"6039_CR1","first-page":"4819","volume":"44","author":"X Zhang","year":"2021","unstructured":"Zhang X (2021) Deep learning-based multi-focus image fusion: A survey and a comparative study. IEEE Trans Pattern Anal Mach Intell 44(9):4819\u20134838","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"15","key":"6039_CR2","doi-asserted-by":"crossref","first-page":"3732","DOI":"10.1364\/OL.459629","volume":"47","author":"W Zhou","year":"2022","unstructured":"Zhou W, He J, Li Y et al (2022) Multi-focus image fusion with enhancement filtering for robust vascular quantification using photoacoustic microscopy. Opt Lett 47(15):3732\u20133735","journal-title":"Opt Lett"},{"issue":"2","key":"6039_CR3","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1364\/BOE.448280","volume":"13","author":"P Manescu","year":"2022","unstructured":"Manescu P, Shaw M, Neary-Zajiczek L et al (2022) Content aware multi-focus image fusion for high-magnification blood film microscopy. Biomed Opt Express 13(2):1005\u20131016","journal-title":"Biomed Opt Express"},{"issue":"12","key":"6039_CR4","doi-asserted-by":"crossref","first-page":"6281","DOI":"10.3390\/app12126281","volume":"12","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Yu L, Zhi C et al (2022) A survey of multi-focus image fusion methods. Appl Sci 12(12):6281","journal-title":"Appl Sci"},{"key":"6039_CR5","doi-asserted-by":"crossref","first-page":"116130","DOI":"10.1016\/j.image.2020.116130","volume":"92","author":"J Tan","year":"2021","unstructured":"Tan J, Zhang T, Zhao L et al (2021) Multi-focus image fusion with geometrical sparse representation. Signal Process Image Commun 92:116130","journal-title":"Signal Process Image Commun"},{"issue":"2","key":"6039_CR6","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.inffus.2011.07.001","volume":"14","author":"S Li","year":"2013","unstructured":"Li S, Kang X, Hu J et al (2013) Image matting for fusion of multi-focus images in dynamic scenes. Inf Fusion 14(2):147\u2013162","journal-title":"Inf Fusion"},{"issue":"7","key":"6039_CR7","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1109\/TIP.2013.2244222","volume":"22","author":"S Li","year":"2013","unstructured":"Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Trans Image Process 22(7):2864\u20132875","journal-title":"IEEE Trans Image Process"},{"key":"6039_CR8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.inffus.2013.11.005","volume":"20","author":"Z Zhou","year":"2014","unstructured":"Zhou Z, Li S, Wang B (2014) Multi-scale weighted gradient-based fusion for multi-focus images. Inf Fusion 20:60\u201372","journal-title":"Inf Fusion"},{"issue":"12","key":"6039_CR9","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/LSP.2016.2618776","volume":"23","author":"Y Liu","year":"2016","unstructured":"Liu Y, Chen X, Ward RK et al (2016) Image fusion with convolutional sparse representation. IEEE Signal Process Lett 23(12):1882\u20131886","journal-title":"IEEE Signal Process Lett"},{"key":"6039_CR10","doi-asserted-by":"crossref","unstructured":"Guan Z, Wang X, Nie R et\u00a0al (2022) Ncdcn: multi-focus image fusion via nest connection and dilated convolution network. Appl Intell 1\u201316","DOI":"10.1007\/s10489-022-03194-z"},{"key":"6039_CR11","doi-asserted-by":"crossref","unstructured":"Choudhary G, Sethi D (2022) From conventional approach to machine learning and deep learning approach: An experimental and comprehensive review of image fusion techniques. Arch Comput Methods Eng 1\u201338","DOI":"10.1007\/s11831-022-09833-5"},{"key":"6039_CR12","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.inffus.2014.05.004","volume":"23","author":"Y Liu","year":"2015","unstructured":"Liu Y, Liu S, Wang Z (2015) Multi-focus image fusion with dense sift. Inf Fusion 23:139\u2013155","journal-title":"Inf Fusion"},{"key":"6039_CR13","doi-asserted-by":"crossref","first-page":"116128","DOI":"10.1016\/j.image.2020.116128","volume":"92","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Zhao P, Ma Y et al (2021) Multi-focus image fusion with joint guided image filtering. Signal Process Image Commun 92:116128","journal-title":"Signal Process Image Commun"},{"key":"6039_CR14","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.image.2018.12.004","volume":"72","author":"X Qiu","year":"2019","unstructured":"Qiu X, Li M, Zhang L et al (2019) Guided filter-based multi-focus image fusion through focus region detection. Signal Process Image Commun 72:35\u201346","journal-title":"Signal Process Image Commun"},{"key":"6039_CR15","doi-asserted-by":"crossref","first-page":"4453","DOI":"10.1007\/s10489-020-02066-8","volume":"51","author":"Z Hu","year":"2021","unstructured":"Hu Z, Liang W, Ding D et al (2021) An improved multi-focus image fusion algorithm based on multi-scale weighted focus measure. Appl Intell 51:4453\u20134469","journal-title":"Appl Intell"},{"issue":"4","key":"6039_CR16","doi-asserted-by":"crossref","first-page":"100774","DOI":"10.1016\/j.irbm.2023.100774","volume":"44","author":"SY Lu","year":"2023","unstructured":"Lu SY, Wang SH, Zhang YD (2023) Bcdnet: An optimized deep network for ultrasound breast cancer detection. IRBM 44(4):100774","journal-title":"IRBM"},{"issue":"3","key":"6039_CR17","doi-asserted-by":"crossref","first-page":"100749","DOI":"10.1016\/j.irbm.2022.100749","volume":"44","author":"SS Chakravarthy","year":"2023","unstructured":"Chakravarthy SS, Bharanidharan N, Rajaguru H (2023) Deep learning-based metaheuristic weighted k-nearest neighbor algorithm for the severity classification of breast cancer. IRBM 44(3):100749","journal-title":"IRBM"},{"key":"6039_CR18","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.inffus.2016.12.001","volume":"36","author":"Y Liu","year":"2017","unstructured":"Liu Y, Chen X, Peng H et al (2017) Multi-focus image fusion with a deep convolutional neural network. Inf Fusion 36:191\u2013207","journal-title":"Inf Fusion"},{"key":"6039_CR19","doi-asserted-by":"crossref","first-page":"5793","DOI":"10.1007\/s00521-020-05358-9","volume":"33","author":"B Ma","year":"2021","unstructured":"Ma B, Zhu Y, Yin X et al (2021) Sesf-fuse: An unsupervised deep model for multi-focus image fusion. Neural Comput Appl 33:5793\u20135804","journal-title":"Neural Comput Appl"},{"key":"6039_CR20","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.inffus.2019.02.003","volume":"51","author":"M Amin-Naji","year":"2019","unstructured":"Amin-Naji M, Aghagolzadeh A, Ezoji M (2019) Ensemble of cnn for multi-focus image fusion. Inf Fusion 51:201\u2013214","journal-title":"Inf Fusion"},{"key":"6039_CR21","doi-asserted-by":"crossref","unstructured":"Li H, Qian W, Nie R et\u00a0al (2023) Siamese conditional generative adversarial network for multi-focus image fusion. Appl Intell 1\u201316","DOI":"10.1007\/s10489-022-04406-2"},{"key":"6039_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 et al (2020) Ifcnn: A general image fusion framework based on convolutional neural network. Inf Fusion 54:99\u2013118","journal-title":"Inf Fusion"},{"issue":"1","key":"6039_CR23","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1109\/TPAMI.2020.3012548","volume":"44","author":"H Xu","year":"2020","unstructured":"Xu H, Ma J, Jiang J et al (2020) U2fusion: A unified unsupervised image fusion network. IEEE Trans Pattern Anal Mach Intell 44(1):502\u2013518","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6039_CR24","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.inffus.2020.08.022","volume":"66","author":"H Zhang","year":"2021","unstructured":"Zhang H, Le Z, Shao Z et al (2021) Mff-gan: An unsupervised generative adversarial network with adaptive and gradient joint constraints for multi-focus image fusion. Inf Fusion 66:40\u201353","journal-title":"Inf Fusion"},{"issue":"2","key":"6039_CR25","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1007\/s10489-022-03658-2","volume":"53","author":"L Ma","year":"2023","unstructured":"Ma L, Hu Y, Zhang B et al (2023) A new multi-focus image fusion method based on multi-classification focus learning and multi-scale decomposition. Appl Intell 53(2):1452\u20131468","journal-title":"Appl Intell"},{"key":"6039_CR26","doi-asserted-by":"crossref","first-page":"116295","DOI":"10.1016\/j.image.2021.116295","volume":"96","author":"Y Wang","year":"2021","unstructured":"Wang Y, Xu S, Liu J et al (2021) Mfif-gan: A new generative adversarial network for multi-focus image fusion. Signal Process Image Commun 96:116295","journal-title":"Signal Process Image Commun"},{"key":"6039_CR27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2022.06.001","volume":"86\u201387","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wang L, Li H et al (2022) Multi-focus image fusion with deep residual learning and focus property detection. Inf Fusion 86\u201387:1\u201316","journal-title":"Inf Fusion"},{"key":"6039_CR28","doi-asserted-by":"crossref","first-page":"966","DOI":"10.1109\/TMM.2021.3134565","volume":"25","author":"F Zhao","year":"2023","unstructured":"Zhao F, Zhao W, Lu H et al (2023) Depth-distilled multi-focus image fusion. IEEE Trans Multimed 25:966\u2013978","journal-title":"IEEE Trans Multimed"},{"key":"6039_CR29","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.inffus.2022.11.014","volume":"92","author":"X Hu","year":"2023","unstructured":"Hu X, Jiang J, Liu X et al (2023) Zmff: Zero-shot multi-focus image fusion. Inf Fusion 92:127\u2013138","journal-title":"Inf Fusion"},{"key":"6039_CR30","first-page":"1","volume":"70","author":"Y Zang","year":"2021","unstructured":"Zang Y, Zhou D, Wang C et al (2021) Ufa-fuse: A novel deep supervised and hybrid model for multifocus image fusion. IEEE Trans Instrum Meas 70:1\u201317","journal-title":"IEEE Trans Instrum Meas"},{"key":"6039_CR31","doi-asserted-by":"crossref","unstructured":"Xu H, Ma J, Le Z et\u00a0al (2020) Fusiondn: A unified densely connected network for image fusion. In: Proceedings of the AAAI conference on artificial intelligence, pp 12484\u201312491","DOI":"10.1609\/aaai.v34i07.6936"},{"key":"6039_CR32","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1016\/j.neucom.2021.10.115","volume":"470","author":"B Ma","year":"2022","unstructured":"Ma B, Yin X, Wu D et al (2022) End-to-end learning for simultaneously generating decision map and multi-focus image fusion result. Neurocomputing 470:204\u2013216","journal-title":"Neurocomputing"},{"issue":"7","key":"6039_CR33","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 et al (2022) Swinfusion: Cross-domain long-range learning for general image fusion via swin transformer. IEEE\/CAA J Autom Sin 9(7):1200\u20131217","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"6039_CR34","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.inffus.2022.11.010","volume":"92","author":"C Cheng","year":"2023","unstructured":"Cheng C, Xu T, Wu XJ (2023) Mufusion: A general unsupervised image fusion network based on memory unit. Inf Fusion 92:80\u201392","journal-title":"Inf Fusion"},{"key":"6039_CR35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.inffus.2021.05.005","volume":"76","author":"H Li","year":"2021","unstructured":"Li H, Zhang B, Zhang Y et al (2021) A defense method based on attention mechanism against traffic sign adversarial samples. Inf Fusion 76:55\u201365","journal-title":"Inf Fusion"},{"key":"6039_CR36","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1109\/TMM.2022.3141933","volume":"25","author":"Y Qiu","year":"2023","unstructured":"Qiu Y, Liu Y, Chen Y et al (2023) A2s ppnet: Attentive atrous spatial pyramid pooling network for salient object detection. IEEE Trans Multimed 25:1991\u20132006","journal-title":"IEEE Trans Multimed"},{"key":"6039_CR37","doi-asserted-by":"crossref","unstructured":"Roy AG, Navab N, Wachinger C (2018) Concurrent spatial and channel \u2018squeeze & excitation\u2019in fully convolutional networks. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, Springer, pp 421\u2013429","DOI":"10.1007\/978-3-030-00928-1_48"},{"issue":"8","key":"6039_CR38","doi-asserted-by":"crossref","first-page":"10883","DOI":"10.1007\/s11042-022-12046-4","volume":"81","author":"N Yu","year":"2022","unstructured":"Yu N, Li J, Hua Z (2022) Attention based dual path fusion networks for multi-focus image. Multimed Tools Appl 81(8):10883\u201310906","journal-title":"Multimed Tools Appl"},{"key":"6039_CR39","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"issue":"6","key":"6039_CR40","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1049\/ipr2.12430","volume":"16","author":"D Zhou","year":"2022","unstructured":"Zhou D, Jin X, Jiang Q et al (2022) Mcrd-net: An unsupervised dense network with multi-scale convolutional block attention for multi-focus image fusion. IET Image Process 16(6):1558\u20131574","journal-title":"IET Image Process"},{"key":"6039_CR41","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 13708\u201313717","DOI":"10.1109\/CVPR46437.2021.01350"},{"issue":"6","key":"6039_CR42","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s00138-022-01345-3","volume":"33","author":"L Jiang","year":"2022","unstructured":"Jiang L, Fan H, Li J (2022) Multi-level receptive field feature reuse for multi-focus image fusion. Mach Vis Appl 33(6):92","journal-title":"Mach Vis Appl"},{"issue":"4","key":"6039_CR43","doi-asserted-by":"crossref","first-page":"2829","DOI":"10.1109\/TPWRS.2020.3048359","volume":"36","author":"Z Li","year":"2021","unstructured":"Li Z, Li Y, Liu Y et al (2021) Deep learning based densely connected network for load forecasting. IEEE Trans Power Syst 36(4):2829\u20132840","journal-title":"IEEE Trans Power Syst"},{"issue":"3","key":"6039_CR44","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1109\/TCDS.2022.3225200","volume":"15","author":"J Ji","year":"2023","unstructured":"Ji J, Li S, Liao X et al (2023) Semantic segmentation based on spatial pyramid pooling and multilayer feature fusion. IEEE Trans Cogn Dev Syst 15(3):1524\u20131535","journal-title":"IEEE Trans Cogn Dev Syst"},{"issue":"9","key":"6039_CR45","doi-asserted-by":"crossref","first-page":"6414","DOI":"10.1109\/TCSVT.2022.3166803","volume":"32","author":"C Liu","year":"2022","unstructured":"Liu C, Ding W, Chen P et al (2022) Rb-net: Training highly accurate and efficient binary neural networks with reshaped point-wise convolution and balanced activation. IEEE Trans Circ Syst Video Technol 32(9):6414\u20136424","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"6039_CR46","doi-asserted-by":"crossref","unstructured":"Dai Y, Gieseke F, Oehmcke S et\u00a0al (2021) Attentional feature fusion. In: Proceedings of the IEEE\/CVF winter conference on applications of computer Vision, pp 3560\u20133569","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"6039_CR47","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.inffus.2014.10.004","volume":"25","author":"M Nejati","year":"2015","unstructured":"Nejati M, Samavi S, Shirani S (2015) Multi-focus image fusion using dictionary-based sparse representation. Inf Fusion 25:72\u201384","journal-title":"Inf Fusion"},{"key":"6039_CR48","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","volume":"111","author":"M Everingham","year":"2015","unstructured":"Everingham M, Eslami SA, Van Gool L et al (2015) The pascal visual object classes challenge: A retrospective. Int J Comput Vision 111:98\u2013136","journal-title":"Int J Comput Vision"},{"key":"6039_CR49","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980"},{"key":"6039_CR50","doi-asserted-by":"crossref","unstructured":"Xydeas CS, Petrovic V et al (2000) Objective image fusion performance measure. Electron Lett 36(4):308\u2013309","DOI":"10.1049\/el:20000267"},{"issue":"10","key":"6039_CR51","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1016\/j.imavis.2007.12.002","volume":"27","author":"Y Chen","year":"2009","unstructured":"Chen Y, Blum RS (2009) A new automated quality assessment algorithm for image fusion. Image Vision Comput 27(10):1421\u20131432","journal-title":"Image Vision Comput"},{"key":"6039_CR52","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/B978-0-12-372529-5.00017-2","volume":"19","author":"Q Wang","year":"2008","unstructured":"Wang Q, Shen Y, Jin J (2008) Performance evaluation of image fusion techniques. Image Fusion Algorithms Appl 19:469\u2013492","journal-title":"Image Fusion Algorithms Appl"},{"key":"6039_CR53","doi-asserted-by":"crossref","unstructured":"Xydeas CS, Petrovic VS (2000) Objective pixel-level image fusion performance measure. In: Sensor fusion: Architectures, algorithms, and applications IV, SPIE, pp 89\u201398","DOI":"10.1117\/12.381668"},{"issue":"2","key":"6039_CR54","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1049\/el:20073460","volume":"43","author":"N Cvejic","year":"2007","unstructured":"Cvejic N, Bull D, Canagarajah C (2007) Metric for multimodal image sensor fusion. Electron Lett 43(2):95\u201396","journal-title":"Electron Lett"},{"issue":"5","key":"6039_CR55","doi-asserted-by":"crossref","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2019","unstructured":"Li H, Wu XJ (2019) Densefuse: A fusion approach to infrared and visible images. IEEE Trans Image Process 28(5):2614\u20132623","journal-title":"IEEE Trans Image Process"},{"key":"6039_CR56","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.infrared.2013.07.010","volume":"61","author":"J Adu","year":"2013","unstructured":"Adu J, Gan J, Wang Y et al (2013) Image fusion based on nonsubsampled contourlet transform for infrared and visible light image. Infrared Phys Technol 61:94\u2013100","journal-title":"Infrared Phys Technol"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06039-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06039-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06039-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T15:06:10Z","timestamp":1737385570000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06039-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,10]]},"references-count":56,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6039"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06039-z","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,10]]},"assertion":[{"value":"12 September 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"113"}}