{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T10:51:54Z","timestamp":1758797514529,"version":"3.44.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s00371-025-04120-3","type":"journal-article","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T10:28:49Z","timestamp":1753957729000},"page":"11571-11588","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A detail preservation fusion framework for infrared\u2013visible images via Bayesian and MDLatLRR"],"prefix":"10.1007","volume":"41","author":[{"given":"Yang","family":"Zhengrun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhang","family":"Chengfang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Xucheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Yue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Ziliang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,31]]},"reference":[{"key":"4120_CR1","doi-asserted-by":"publisher","unstructured":"Liu, G., Lin, Z., Yu, Y.: Robust subspace segmentation by low-rank representation. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10) (2010). https:\/\/doi.org\/10.1109\/TCYB.2013.2286106","DOI":"10.1109\/TCYB.2013.2286106"},{"key":"4120_CR2","doi-asserted-by":"publisher","first-page":"211164","DOI":"10.1109\/ACCESS.2020.3036620","volume":"8","author":"G Li","year":"2020","unstructured":"Li, G., Xie, H., Yan, W., Chang, Y., Qu, X.: Detection of road objects with small appearance in images for autonomous driving in various traffic situations using a deep learning based approach. IEEE Access 8, 211164\u2013211172 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3036620","journal-title":"IEEE Access"},{"key":"4120_CR3","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.inffus.2017.10.007","volume":"42","author":"Y Liu","year":"2018","unstructured":"Liu, Y., Chen, X., Wang, Z., Wang, Z.J., Ward, R.K., Wang, X.: Deep learning for pixel-level image fusion: recent advances and future prospects. Inf. Fusion 42, 158\u2013173 (2018). https:\/\/doi.org\/10.1016\/j.inffus.2017.10.007","journal-title":"Inf. Fusion"},{"key":"4120_CR4","unstructured":"Li, H., & Wu, X.-J. (2022). Infrared and visible image fusion using latent low-rank representation. arXiv:1804.08992. doi:10.48550\/arXiv.1804.08992"},{"key":"4120_CR5","doi-asserted-by":"publisher","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.: Pixel-level image fusion: a survey of the state of the art. Inf. Fusion 33, 100\u2013112 (2017). https:\/\/doi.org\/10.1016\/j.inffus.2016.05.004","journal-title":"Inf. Fusion"},{"key":"4120_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.inffus.2015.11.003","volume":"30","author":"Z Zhou","year":"2016","unstructured":"Zhou, Z., Wang, B., Li, S., Dong, M.: Perceptual fusion of infrared and visible images through a hybrid multi-scale decomposition with gaussian and bilateral filters. Inf. Fusion 30, 15\u201326 (2016). https:\/\/doi.org\/10.1016\/j.inffus.2015.11.003","journal-title":"Inf. Fusion"},{"issue":"3","key":"4120_CR7","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/S1566-2535(03)00046-0","volume":"4","author":"G Piella","year":"2003","unstructured":"Piella, G.: A general framework for multiresolution image fusion: from pixels to regions. Inf. Fusion 4(3), 259\u2013280 (2003). https:\/\/doi.org\/10.1016\/S1566-2535(03)00046-0","journal-title":"Inf. Fusion"},{"key":"4120_CR8","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1364\/JOSAA.32.001643","volume":"32","author":"X Yan","year":"2015","unstructured":"Yan, X., Qin, H., Li, J., Zhou, H., Zong, J.: Infrared and visible image fusion with spectral graph wavelet transform. J. Opt. Soc. Am. A 32, 1643 (2015). https:\/\/doi.org\/10.1364\/JOSAA.32.001643","journal-title":"J. Opt. Soc. Am. A"},{"key":"4120_CR9","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.inffus.2017.05.006","volume":"40","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Liu, Y., Blum, R.S., Han, J., Tao, D.: Sparse representation based multi-sensor image fusion for multi-focus and multi-modality images: a review. Inf. Fusion 40, 57\u201375 (2018). https:\/\/doi.org\/10.1016\/j.inffus.2017.05.006","journal-title":"Inf. Fusion"},{"key":"4120_CR10","doi-asserted-by":"publisher","first-page":"306","DOI":"10.3390\/e19070306","volume":"19","author":"K Wang","year":"2017","unstructured":"Wang, K., Qi, G., Zhu, Z., Chai, Y.: A novel geometric dictionary construction approach for sparse representation based image fusion. Entropy 19, 306 (2017). https:\/\/doi.org\/10.3390\/e19070306","journal-title":"Entropy"},{"key":"4120_CR11","doi-asserted-by":"publisher","first-page":"8515","DOI":"10.1016\/j.eswa.2011.01.052","volume":"38","author":"H Jiang","year":"2011","unstructured":"Jiang, H., Tian, Y.: Fuzzy image fusion based on modified Self-Generating Neural Network. Expert Syst. Appl. 38, 8515\u20138523 (2011). https:\/\/doi.org\/10.1016\/j.eswa.2011.01.052","journal-title":"Expert Syst. Appl."},{"key":"4120_CR12","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.: FusionGAN: a generative adversarial network for infrared and visible image fusion. Inf. Fusion 48, 11\u201326 (2019). https:\/\/doi.org\/10.1016\/j.inffus.2018.09.004","journal-title":"Inf. Fusion"},{"key":"4120_CR13","doi-asserted-by":"publisher","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.: IFCNN: a general image fusion framework based on convolutional neural network. Inf. Fusion 54, 99\u2013118 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.07.011","journal-title":"Inf. Fusion"},{"key":"4120_CR14","doi-asserted-by":"publisher","unstructured":"Bavirisetti, D.P., Xiao, G., Liu, G.: Multi-sensor image fusion based on fourth order partial differential equations, In: 2017 20th International Conference on Information Fusion, Xi\u2019an, China, IEEE, pp. 1\u20139. (2017). https:\/\/doi.org\/10.23919\/ICIF.2017.8009719","DOI":"10.23919\/ICIF.2017.8009719"},{"key":"4120_CR15","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1109\/JSEN.2007.894926","volume":"7","author":"N Cvejic","year":"2007","unstructured":"Cvejic, N., Bull, D., Canagarajah, N.: Region-based multimodal image fusion using ICA bases. IEEE Sens. J. 7, 743\u2013751 (2007). https:\/\/doi.org\/10.1109\/JSEN.2007.894926","journal-title":"IEEE Sens. J."},{"key":"4120_CR16","doi-asserted-by":"publisher","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.: Infrared and visible image fusion with the use of multi-scale edge-preserving decomposition and guided image filter. Infrared Phys. Technol. 72, 37\u201351 (2015). https:\/\/doi.org\/10.1016\/j.infrared.2015.07.003","journal-title":"Infrared Phys. Technol."},{"key":"4120_CR17","doi-asserted-by":"publisher","first-page":"1650024","DOI":"10.1142\/S0219691316500247","volume":"14","author":"Y Bin","year":"2016","unstructured":"Bin, Y., Chao, Y., Guoyu, H.: Efficient image fusion with approximate sparse representation. Int. J. Wavelets Multiresolut. Inf. Process. 14, 1650024 (2016). https:\/\/doi.org\/10.1142\/S0219691316500247","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"4120_CR18","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1109\/TIM.2017.2658098","volume":"66","author":"Y Yang","year":"2017","unstructured":"Yang, Y., Que, Y., Huang, S., Lin, P.: Multiple visual features measurement with gradient domain guided filtering for multisensor image fusion. IEEE Trans. Instrum. Meas. 66, 691\u2013703 (2017). https:\/\/doi.org\/10.1109\/TIM.2017.2658098","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4120_CR19","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.: MDLatLRR: a novel decomposition method for infrared and visible image fusion. IEEE Trans. Image Process. 29, 4733\u20134746 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2975984","journal-title":"IEEE Trans. Image Process."},{"key":"4120_CR20","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600\u2013612 (2004). https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans. Image Process."},{"key":"4120_CR21","doi-asserted-by":"publisher","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.: Bayesian fusion for infrared and visible images. Signal Process. 177, 107734 (2020). https:\/\/doi.org\/10.1016\/j.sigpro.2020.107734","journal-title":"Signal Process."},{"key":"4120_CR22","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.: MDLatLRR: a novel decomposition method for infrared and visible image fusion. IEEE Trans. Image Process. 29, 4733\u20134746 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2975984","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"4120_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc.: Ser. B (Methodol.) 39(1), 1\u201322 (1977). https:\/\/doi.org\/10.1111\/j.2517-6161.1977.tb01600.x","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"4120_CR24","doi-asserted-by":"publisher","first-page":"4209","DOI":"10.1007\/s11760-023-02653-5","volume":"17","author":"B-L Jian","year":"2023","unstructured":"Jian, B.-L., Tu, C.-C.: Multi-level optimal fusion algorithm for infrared and visible image. SIViP 17, 4209\u20134217 (2023). https:\/\/doi.org\/10.1007\/s11760-023-02653-5","journal-title":"SIViP"},{"key":"4120_CR25","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.: An infrared and visible image fusion method based on multi-scale transformation and norm optimization. Inf. Fusion 71, 109\u2013129 (2021). https:\/\/doi.org\/10.1016\/j.inffus.2021.02.008","journal-title":"Inf. Fusion"},{"key":"4120_CR26","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.infrared.2004.03.011","volume":"46","author":"C Ibarra-Castanedo","year":"2004","unstructured":"Ibarra-Castanedo, C., Gonz\u00e1lez, D., Klein, M., Pilla, M., Vallerand, S., Maldague, X.: Infrared image processing and data analysis. Infrared Phys. Technol. 46, 75\u201383 (2004). https:\/\/doi.org\/10.1016\/j.infrared.2004.03.011","journal-title":"Infrared Phys. Technol."},{"key":"4120_CR27","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.: Infrared and visible image fusion methods and applications: a survey. Inf. Fusion 45, 153\u2013178 (2019). https:\/\/doi.org\/10.1016\/j.inffus.2018.02.004","journal-title":"Inf. Fusion"},{"key":"4120_CR28","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.inffus.2010.03.002","volume":"12","author":"S Li","year":"2011","unstructured":"Li, S., Yang, B., Hu, J.: Performance comparison of different multi-resolution transforms for image fusion. Inf. Fusion 12, 74\u201384 (2011). https:\/\/doi.org\/10.1016\/j.inffus.2010.03.002","journal-title":"Inf. Fusion"},{"key":"4120_CR29","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1109\/26.477498","volume":"43","author":"AM Eskicioglu","year":"1995","unstructured":"Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43, 2959\u20132965 (1995). https:\/\/doi.org\/10.1109\/26.477498","journal-title":"IEEE Trans. Commun."},{"key":"4120_CR30","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.optcom.2014.12.032","volume":"341","author":"G Cui","year":"2015","unstructured":"Cui, G., Feng, H., Xu, Z., Li, Q., Chen, Y.: Detail preserved fusion of visible and infrared images using regional saliency extraction and multi-scale image decomposition. Optics Commun. 341, 199\u2013209 (2015). https:\/\/doi.org\/10.1016\/j.optcom.2014.12.032","journal-title":"Optics Commun."},{"key":"4120_CR31","doi-asserted-by":"publisher","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.: A new image fusion performance metric based on visual information fidelity. Inf. Fusion 14, 127\u2013135 (2013). https:\/\/doi.org\/10.1016\/j.inffus.2011.08.002","journal-title":"Inf. Fusion"},{"key":"4120_CR32","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1049\/el:20020212","volume":"38","author":"G Qu","year":"2002","unstructured":"Qu, G., Zhang, D., Yan, P.: Information measure for performance of image fusion. Electron. Lett. 38, 313\u2013315 (2002). https:\/\/doi.org\/10.1049\/el:20020212","journal-title":"Electron. Lett."},{"key":"4120_CR33","doi-asserted-by":"publisher","DOI":"10.1117\/1.2945910","volume":"2","author":"J Van Aardt","year":"2008","unstructured":"Van Aardt, J.: Assessment of image fusion procedures using entropy, image quality, and multispectral classification. J. Appl. Remote Sens. 2, 023522 (2008). https:\/\/doi.org\/10.1117\/1.2945910","journal-title":"J. Appl. Remote Sens."},{"key":"4120_CR34","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1023\/B:APIN.0000033637.51909.04","volume":"7","author":"H Wang","year":"2004","unstructured":"Wang, H., Zhong, W., Wang, J., Xia, D.: Research of measure for digital image definition. J. Image Graphics 7, 828\u2013831 (2004). https:\/\/doi.org\/10.1023\/B:APIN.0000033637.51909.04","journal-title":"J. Image Graphics"},{"key":"4120_CR35","doi-asserted-by":"publisher","unstructured":"Meng, Z., Li, H., Zhang, Z., Shen, Z., Yu, Y., Song, X., Wu, X.: CoMoFusion: fast and high-quality fusion of infrared and visible image with consistency model, In: Computer Science $$>$$ Computer Vision and Pattern Recognition, submitted on 31 May 2024, revised 12 Jun 2024. https:\/\/doi.org\/10.48550\/arXiv.2405.20764","DOI":"10.48550\/arXiv.2405.20764"},{"key":"4120_CR36","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., Zhang, X.-P.: DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Trans. Image Process. 29, 4980\u20134995 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2977573","journal-title":"IEEE Trans. Image Process."},{"key":"4120_CR37","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Bai, H., Zhu, Y., Zhang, J., Xu, S., Zhang, Y., Zhang, K., Meng, D., Timofte, R., Van\u00a0Gool, L.: DDFM: denoising diffusion model for multi-modality image fusion, In: Computer Science $$>$$ Computer Vision and Pattern Recognition, submitted on 13 Mar 2023, revised 22 Aug (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.06840","DOI":"10.48550\/arXiv.2303.06840"},{"key":"4120_CR38","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2019","unstructured":"Li, H., Wu, X.-J.: DenseFuse: a fusion approach to infrared and visible images. IEEE Trans. Image Process. 28, 2614\u20132623 (2019). https:\/\/doi.org\/10.1109\/TIP.2018.2887342","journal-title":"IEEE Trans. Image Process."},{"key":"4120_CR39","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Xu, S., Zhang, C., Liu, J., Li, P., Zhang, J.: DIDFuse: deep image decomposition for infrared and visible image fusion, In: Electrical Engineering and Systems Science $$>$$ Image and Video Processing, submitted on 20 Mar 2020, revised 8 Apr (2021). https:\/\/doi.org\/10.48550\/arXiv.2003.09210","DOI":"10.48550\/arXiv.2003.09210"},{"key":"4120_CR40","doi-asserted-by":"publisher","unstructured":"Li, H., Fu, Y.: FCDFusion: a Fast, Low color deviation method for fusing visible and infrared image pairs, In: Computer Science $$>$$ Computer Vision and Pattern Recognition, submitted on 2 Aug (2024). https:\/\/doi.org\/10.48550\/arXiv.2408.01080","DOI":"10.48550\/arXiv.2408.01080"},{"key":"4120_CR41","doi-asserted-by":"publisher","unstructured":"Deng, Y., Xu, T., Cheng, C., X.-J. Wu, J. Kittler, MMDRFuse: distilled mini-model with dynamic refresh for multi-modality image fusion, In: Computer Science $$>$$ Computer Vision and Pattern Recognition, submitted on 28 Aug 2024. https:\/\/doi.org\/10.48550\/arXiv.2408.15641","DOI":"10.48550\/arXiv.2408.15641"},{"key":"4120_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2024.108094","volume":"176","author":"H Tang","year":"2024","unstructured":"Tang, H., Qian, Y., Xing, M., Cao, Y., Liu, G.: MPCFusion: multi-scale parallel cross fusion for infrared and visible images via convolution and vision transformer. Opt. Lasers Eng. 176, 108094 (2024). https:\/\/doi.org\/10.1016\/j.optlaseng.2024.108094","journal-title":"Opt. Lasers Eng."},{"key":"4120_CR43","doi-asserted-by":"publisher","unstructured":"Ma, H., Li, H., Cheng, C., Wang, G., Song, X., Wu, X.: S4Fusion: saliency-aware selective state space model for infrared visible image fusion, In: Computer Science $$>$$ Computer Vision and Pattern Recognition, submitted on 31 May 2024 (v1), last revised 3 Jun 2024 (v2). https:\/\/doi.org\/10.48550\/arXiv.2405.20881","DOI":"10.48550\/arXiv.2405.20881"},{"issue":"1","key":"4120_CR44","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.: U2Fusion: a unified unsupervised image fusion network. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 502\u2013518 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2020.3012548","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04120-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-025-04120-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-025-04120-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T14:03:21Z","timestamp":1758722601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-025-04120-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,31]]},"references-count":44,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["4120"],"URL":"https:\/\/doi.org\/10.1007\/s00371-025-04120-3","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2025,7,31]]},"assertion":[{"value":"14 July 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","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 Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}