{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T02:45:46Z","timestamp":1778726746945,"version":"3.51.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 61805063"],"award-info":[{"award-number":["No. 61805063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Graduate Scientific Research Foundation of Hangzhou Dianzi University","award":["No.CXJJ2023058"],"award-info":[{"award-number":["No.CXJJ2023058"]}]},{"name":"the Natural Science Foundation of Zhejiang Province","award":["No. LY22F050002"],"award-info":[{"award-number":["No. LY22F050002"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00371-024-03390-7","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:02:54Z","timestamp":1714521774000},"page":"3849-3869","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel dynamic scene deblurring framework based on hybrid activation and edge-assisted dual-branch residuals"],"prefix":"10.1007","volume":"40","author":[{"given":"Zihan","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangmang","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jufeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"3390_CR1","doi-asserted-by":"crossref","unstructured":"Hyun Kim T, Ahn B, Mu Lee K.: Dynamic scene deblurring. In: Proceedings of the IEEE international conference on computer vision, pp. 3160\u20133167 (2013)","DOI":"10.1109\/ICCV.2013.392"},{"key":"3390_CR2","doi-asserted-by":"crossref","unstructured":"Xu L, Zheng S, Jia J.: Unnatural l0 sparse representation for natural image deblurring. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1107\u20131114 (2013)","DOI":"10.1109\/CVPR.2013.147"},{"key":"3390_CR3","doi-asserted-by":"crossref","unstructured":"Pan J, Sun D, Pfister H, et al.: Blind image deblurring using dark channel prior. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1628\u20131636 (2016)","DOI":"10.1109\/CVPR.2016.180"},{"key":"3390_CR4","doi-asserted-by":"crossref","unstructured":"Zhou L, Tang J.: Fraction-order total variation blind image restoration based on L1-norm. In: Applied Mathematical Modelling, 51, pp. 469\u2013476 (2017)","DOI":"10.1016\/j.apm.2017.07.009"},{"key":"3390_CR5","doi-asserted-by":"crossref","unstructured":"Chen L, Fang F, Wang T, et al.: Blind image deblurring with local maximum gradient prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1742\u20131750 (2019)","DOI":"10.1109\/CVPR.2019.00184"},{"key":"3390_CR6","unstructured":"L. Xu, J. Ren, C. Liu, J. Jia.: Deep convolutional neural network for image deconvolution. In: International Conference on Neural Information Processing Systems, pp. 1790\u20131798 (2014)"},{"key":"3390_CR7","doi-asserted-by":"publisher","unstructured":"J. Sun, Wenfei Cao, Zongben Xu, J. Ponce.: Learning a convolutional neural network for non-uniform motion Blur removal. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 769\u2013777 (2015). https:\/\/doi.org\/10.1109\/CVPR.2015.7298677.","DOI":"10.1109\/CVPR.2015.7298677"},{"key":"3390_CR8","doi-asserted-by":"publisher","unstructured":"S. Cho, Jue Wang, S. Lee.: Handling outliers in non-blind image deconvolution. In: 2011 International Conference on Computer Vision, pp. 495\u2013502 (2011). https:\/\/doi.org\/10.1109\/ICCV.2011.6126280.","DOI":"10.1109\/ICCV.2011.6126280"},{"key":"3390_CR9","doi-asserted-by":"publisher","unstructured":"C. J. Schuler, M. Hirsch, S. Harmeling, B. Sch\u00f6lkopf.: Learning to Deblur. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7), pp.1439\u20131451 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2015.2481418.","DOI":"10.1109\/TPAMI.2015.2481418"},{"key":"3390_CR10","doi-asserted-by":"publisher","unstructured":"A. Chakrabarti.: A neural approach to blind motion deblurring. In: European Conference on Computer Vision, pp. 221\u2013235 (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_14.","DOI":"10.1007\/978-3-319-46487-9_14"},{"key":"3390_CR11","doi-asserted-by":"publisher","unstructured":"L. Li, J. Pan, W. Lai, C. Gao, N. Sang, M. Yang.: Learning a discriminative prior for blind image deblurring. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6616\u20136625 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00692.","DOI":"10.1109\/CVPR.2018.00692"},{"key":"3390_CR12","doi-asserted-by":"publisher","unstructured":"Park, Seung-won, Cheol-Hwan Yoo, Yong-Goo Shin.: Effective shortcut technique for generative adversarial networks. In: Applied Intelligence, pp. 2055\u20132067 (2023). https:\/\/doi.org\/10.1007\/s10489-022-03666-2","DOI":"10.1007\/s10489-022-03666-2"},{"key":"3390_CR13","doi-asserted-by":"publisher","unstructured":"O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, J. Matas.: Deblurgan: Blind motion deblurring using conditional adversarial networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8183\u20138192 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00854.","DOI":"10.1109\/CVPR.2018.00854"},{"key":"3390_CR14","doi-asserted-by":"publisher","unstructured":"S. Nah, T. H. Kim, K. M. Lee.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 257\u2013265 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.35.","DOI":"10.1109\/CVPR.2017.35"},{"key":"3390_CR15","doi-asserted-by":"publisher","unstructured":"J. Zhang, J. Pan, J. Ren, et al.: Dynamic scene deblurring using spatially variant recurrent neural networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2521\u20132529. (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00267.","DOI":"10.1109\/CVPR.2018.00267"},{"key":"3390_CR16","doi-asserted-by":"publisher","unstructured":"X. Tao, H. Gao, X. Shen, J. Wang, J. Jia.: Scale-recurrent network for deep image deblurring. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8174\u20138182. (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00853.","DOI":"10.1109\/CVPR.2018.00853"},{"key":"3390_CR17","doi-asserted-by":"publisher","unstructured":"O. Kupyn, T. Martyniuk, J. Wu, Z. Wang.: DeblurGAN-v2: deblurring (orders-of-magnitude) faster and better. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8877\u20138886. (2019) https:\/\/doi.org\/10.1109\/ICCV.2019.00897.","DOI":"10.1109\/ICCV.2019.00897"},{"key":"3390_CR18","doi-asserted-by":"publisher","unstructured":"Park, D., Kang, D.U., Kim, J., Chun, S.Y.: Multi-temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training. In: European Conference on Computer Vision, Springer, pp. 327\u2013343 (2020). https:\/\/doi.org\/10.1007\/978-3-030-58539-6_20.","DOI":"10.1007\/978-3-030-58539-6_20"},{"key":"3390_CR19","doi-asserted-by":"publisher","unstructured":"S. J. Cho, S. W. Ji, J. P. Hong, S. W. Jung, S. J. Ko.: Rethinking coarse-to-fine approach in single image deblurring. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4621\u20134630. (2021) https:\/\/doi.org\/10.1109\/ICCV48922.2021.00460.","DOI":"10.1109\/ICCV48922.2021.00460"},{"key":"3390_CR20","doi-asserted-by":"publisher","unstructured":"H. Zhang, Y. Dai, H. Li, P. Koniusz.: Deep stacked hierarchical multi-patch network for image deblurring. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5971\u20135979. (2019) https:\/\/doi.org\/10.1109\/CVPR.2019.00613.","DOI":"10.1109\/CVPR.2019.00613"},{"key":"3390_CR21","doi-asserted-by":"publisher","unstructured":"S. Zheng, Z. Zhu, J. Cheng, Y. Guo, Y. Zhao.: Edge heuristic GAN for non-uniform blind deblurring. In: IEEE Signal Processing Letters, pp. 1546\u20131550 (2019). https:\/\/doi.org\/10.1109\/LSP.2019.2939752.","DOI":"10.1109\/LSP.2019.2939752"},{"key":"3390_CR22","doi-asserted-by":"publisher","unstructured":"J. Hu et al.: Multi-scale feature fusion algorithm based on generative adversarial network for motion deblurring. In: 2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), pp. 294\u2013299 (2023). https:\/\/doi.org\/10.1109\/RAIIC59453.2023.10280859.","DOI":"10.1109\/RAIIC59453.2023.10280859"},{"key":"3390_CR23","doi-asserted-by":"publisher","unstructured":"J. Peng, T. Guan, F. Liu, J. Liang.: MND-GAN: A research on image deblurring algorithm based on generative adversarial network. In: 2023 42nd Chinese Control Conference (CCC), pp. 7584\u20137589 (2023). https:\/\/doi.org\/10.23919\/CCC58697.2023.10240483.","DOI":"10.23919\/CCC58697.2023.10240483"},{"key":"3390_CR24","doi-asserted-by":"crossref","unstructured":"Whang J, Delbracio M, Talebi H, et al.: Deblurring via stochastic refinement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16293\u201316303 (2022).","DOI":"10.1109\/CVPR52688.2022.01581"},{"key":"3390_CR25","doi-asserted-by":"crossref","unstructured":"Ren M, Delbracio M, Talebi H, et al. Multiscale structure guided diffusion for image deblurring. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10721\u201310733 (2023).","DOI":"10.1109\/ICCV51070.2023.00984"},{"key":"3390_CR26","unstructured":"Chen Z, Zhang Y, Liu D, et al. Hierarchical integration diffusion model for realistic image deblurring. In: Advances in Neural Information Processing Systems, 36(2024)."},{"key":"3390_CR27","doi-asserted-by":"publisher","unstructured":"F. J. Tsai, Y. T. Peng, C. C. Tsai, Y. Y. Lin, C. W. Lin.: BANet: a blur-aware attention network for dynamic scene deblurring. In: IEEE Transactions on Image Processing 31, pp. 6789\u20136799 (2022). https:\/\/doi.org\/10.1109\/TIP.2022.3216216.","DOI":"10.1109\/TIP.2022.3216216"},{"key":"3390_CR28","doi-asserted-by":"publisher","unstructured":"D. Yang, M. Yamac.: Motion aware double Attention network for dynamic scene deblurring. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1112\u20131122 (2022). https:\/\/doi.org\/10.1109\/CVPRW56347.2022.00120.","DOI":"10.1109\/CVPRW56347.2022.00120"},{"key":"3390_CR29","doi-asserted-by":"crossref","unstructured":"Zhang J, Cui G, Zhao J, et al.: High-frequency attention residual GAN network for blind motion deblurring. In: IEEE Access, pp. 81390\u201381405, (2022).","DOI":"10.1109\/ACCESS.2022.3194524"},{"key":"3390_CR30","doi-asserted-by":"crossref","unstructured":"Khan R A, Luo Y, Wu F X.: Multi\u2010scale GAN with residual image learning for removing heterogeneous blur. In: IET Image Processing, pp. 2412\u20132431, (2022).","DOI":"10.1049\/ipr2.12497"},{"key":"3390_CR31","doi-asserted-by":"crossref","unstructured":"Chen L, Chu X, Zhang X, et al.: Simple baselines for image restoration. In: European Conference on Computer Vision. Cham: Springer Nature Switzerland, pp. 17\u201333, (2022).","DOI":"10.1007\/978-3-031-20071-7_2"},{"key":"3390_CR32","unstructured":"Ruan L, Bemana M, Seidel H, et al.: Revisiting image Deblurring with an efficient ConvNet. arXiv preprint arXiv:2302.02234, (2023)."},{"key":"3390_CR33","doi-asserted-by":"crossref","unstructured":"Li S, Liu M, Zhang Y, et al.: SAM-Deblur: Let segment anything boost image Deblurring. arXiv preprint arXiv:2309.02270, (2023).","DOI":"10.1109\/ICASSP48485.2024.10445844"},{"key":"3390_CR34","doi-asserted-by":"crossref","unstructured":"Chen K, Yu L.: Motion Deblur by learning residual from events. In: IEEE Transactions on Multimedia, (2024).","DOI":"10.1109\/TMM.2024.3355630"},{"key":"3390_CR35","doi-asserted-by":"publisher","unstructured":"K. He, X. Zhang, S. Ren, J. Sun.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"3390_CR36","doi-asserted-by":"publisher","unstructured":"Abuolaim, A., Afifi, M., Brown, M.S.: Improving single-image defocus deblurring: how dual-pixel images help through multi-task learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1231\u20131239 (2022). https:\/\/doi.org\/10.1109\/WACV51458.2022.00016.","DOI":"10.1109\/WACV51458.2022.00016"},{"key":"3390_CR37","doi-asserted-by":"publisher","unstructured":"G. Carbajal, P. Vitoria, J. Lezama, P. Mus\u00e9.: Blind motion deblurring with pixel-wise Kernel estimation via Kernel prediction networks. In: IEEE Transactions on Computational Imaging, pp. 928\u2013943 (2023). https:\/\/doi.org\/10.1109\/TCI.2023.3322012.","DOI":"10.1109\/TCI.2023.3322012"},{"key":"3390_CR38","doi-asserted-by":"publisher","unstructured":"Zihan Li, Guangmang Cui, Jufeng Zhao, Qinlei Xiang, Bintao He.: Joint strong edge and multi-stream adaptive fusion network for non-uniform image deblurring. In: Journal of Visual Communication and Image Representation, pp.1047\u20133203 (2022). https:\/\/doi.org\/10.1016\/j.jvcir.2022.103663.","DOI":"10.1016\/j.jvcir.2022.103663"},{"key":"3390_CR39","doi-asserted-by":"publisher","unstructured":"I. Goodfellow, J. Pouget-Abadie, M. Mirza, B.Xu, et al.: Generative adversarial nets. In: Neural Information Processing Systems, pp. 2672\u20132680 (2014). https:\/\/doi.org\/10.48550\/arXiv.1406.2661.","DOI":"10.48550\/arXiv.1406.2661"},{"issue":"3\u20134","key":"3390_CR40","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.1002\/cav.2066","volume":"33","author":"C Guo","year":"2022","unstructured":"Guo, C., Wang, Q., Dai, H.-N., Li, P.: Vdn: Variant-depth network for motion deblurring. Comp. Animati. Virtual Worlds 33(3\u20134), 2066 (2022). https:\/\/doi.org\/10.1002\/cav.2066","journal-title":"Comp. Animati. Virtual Worlds"},{"key":"3390_CR41","doi-asserted-by":"publisher","unstructured":"P. Isola, J. Zhu, T. Zhou, A. A.: Efros. Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967\u20135976 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.632.","DOI":"10.1109\/CVPR.2017.632"},{"key":"3390_CR42","doi-asserted-by":"publisher","unstructured":"Huang, Gao, Zhuang Liu, Kilian Q. Weinberger.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2016). https:\/\/doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"3390_CR43","doi-asserted-by":"publisher","unstructured":"S. Nah et al.: NTIRE 2019 Challenge on video deblurring and super-resolution: dataset and study. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1996\u20132005 (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00251.","DOI":"10.1109\/CVPRW.2019.00251"},{"key":"3390_CR44","unstructured":"Glorot, Xavier, Antoine Bordes, Yoshua Bengio.: Deep Sparse Rectifier Neural Networks. In: Journal of Machine Learning Research, pp. 315\u2013323 (2011)"},{"key":"3390_CR45","doi-asserted-by":"publisher","unstructured":"K. Simonyan, A. Zisserman.: Very deep convolutional networks for large-scale image recognition. In: Computer Science. (2014). https:\/\/doi.org\/10.48550\/arXiv.1409.1556.","DOI":"10.48550\/arXiv.1409.1556"},{"key":"3390_CR46","doi-asserted-by":"publisher","unstructured":"J. Johnson, A. Alahi, L. Fei-Fei.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694\u2013711 (2016). https:\/\/doi.org\/10.48550\/arXiv.1603.08155.","DOI":"10.48550\/arXiv.1603.08155"},{"key":"3390_CR47","doi-asserted-by":"publisher","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E.: Squeeze-and-excitation networks. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 2011\u20132023 (2020). https:\/\/doi.org\/10.1109\/CVPR.2018.00745.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3390_CR48","doi-asserted-by":"publisher","unstructured":"S. Woo, J. Park, J. Y. Lee, I. S. Kweon.: CBAM: Convolutional Block Attention Module. In: European Conference on Computer Vision, pp. 3\u201319 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3390_CR49","doi-asserted-by":"publisher","unstructured":"M. Arjovsky, S. Chintala, L. Bottou.: Wasserstein GAN. (2017). https:\/\/doi.org\/10.48550\/arXiv.1701.07875.","DOI":"10.48550\/arXiv.1701.07875"},{"key":"3390_CR50","doi-asserted-by":"publisher","unstructured":"I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. Courville.: Improved training of Wasserstein GANs. In: the 31st International Conference on Neural Information Processing Systerms, pp.5769\u20135779 (2017). https:\/\/doi.org\/10.48550\/arXiv.2109.05652.","DOI":"10.48550\/arXiv.2109.05652"},{"key":"3390_CR51","doi-asserted-by":"publisher","unstructured":"He, Kaiming, X. Zhang, Shaoqing Ren, Jian Sun.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp.1026\u20131034 (2015). https:\/\/doi.org\/10.48550\/arXiv.1502.01852","DOI":"10.48550\/arXiv.1502.01852"},{"key":"3390_CR52","doi-asserted-by":"publisher","unstructured":"H. Sim, M. Kim.: A deep motion deblurring network based on per-pixel adaptive kernels with residual down-up and up-down modules. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2140\u20132149 (2019). https:\/\/doi.org\/10.1109\/CVPRW.2019.00267.","DOI":"10.1109\/CVPRW.2019.00267"},{"key":"3390_CR53","doi-asserted-by":"publisher","unstructured":"Y. Wang, X. Guo, P. Liu, B. Wei.: Up and down Rresidual blocks for convolutional generative adversarial networks. In: IEEE Access, pp. 26051\u201326058 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3056572.","DOI":"10.1109\/ACCESS.2021.3056572"},{"key":"3390_CR54","doi-asserted-by":"publisher","unstructured":"S. Yae, M. Ikehara.: Inverted residual fourier transformation for lightweight single image deblurring. In: 2023 IEEE International Conference on Consumer Electronics (ICCE), pp. 1\u20135 (2023). https:\/\/doi.org\/10.1109\/ICCE56470.2023.10043510.","DOI":"10.1109\/ICCE56470.2023.10043510"},{"key":"3390_CR55","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A.: YOLOv3: an incremental improvement. (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.02767.","DOI":"10.48550\/arXiv.1804.02767"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03390-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-024-03390-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-024-03390-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T11:23:44Z","timestamp":1717673024000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-024-03390-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":55,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["3390"],"URL":"https:\/\/doi.org\/10.1007\/s00371-024-03390-7","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"value":"0178-2789","type":"print"},{"value":"1432-2315","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,30]]},"assertion":[{"value":"26 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 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 declare that they have no conflict of  interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}