{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:05:01Z","timestamp":1740107101217,"version":"3.37.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Vis Comput"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00371-021-02193-4","type":"journal-article","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T20:03:00Z","timestamp":1625515380000},"page":"3643-3665","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Compact and progressive network for enhanced single image super-resolution\u2014ComPrESRNet"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3727-5484","authenticated-orcid":false,"given":"Vishal","family":"Chudasama","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7087-6276","authenticated-orcid":false,"given":"Kishor","family":"Upla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9489-5161","authenticated-orcid":false,"given":"Kiran","family":"Raja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0484-3956","authenticated-orcid":false,"given":"Raghavendra","family":"Ramachandra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9159-2923","authenticated-orcid":false,"given":"Christoph","family":"Busch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,5]]},"reference":[{"key":"2193_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: IEEE Conference on CVPR Workshops, vol.\u00a03, p.\u00a02 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2193_CR2","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., Sohn, K.A.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: ECCV), pp. 252\u2013268 (2018)","DOI":"10.1109\/CVPRW.2018.00123"},{"key":"2193_CR3","unstructured":"Anwar, S., Khan, S., Barnes, N.: A deep journey into super-resolution: A survey. CoRR abs\/1904.07523 (2019)"},{"key":"2193_CR4","unstructured":"Barron, J.T.: A more general robust loss function. arXiv preprint arXiv:1701.03077 (2017)"},{"key":"2193_CR5","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., Alberi-Morel, M.L.: Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: BMVC (2012)","DOI":"10.5244\/C.26.135"},{"key":"2193_CR6","doi-asserted-by":"crossref","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: IEEE conference on CVPR, pp. 1251\u20131258 (2017)","DOI":"10.1109\/CVPR.2017.195"},{"key":"2193_CR7","doi-asserted-by":"crossref","unstructured":"Chudasama, V., Upla, K.: E-prosrnet: An enhanced progressive single image super-resolution approach. CVIU p. 103038 (2020)","DOI":"10.1016\/j.cviu.2020.103038"},{"key":"2193_CR8","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., Zhang, L.: Second-order attention network for single image super-resolution. In: IEEE Conference on CVPR, pp. 11065\u201311074 (2019)","DOI":"10.1109\/CVPR.2019.01132"},{"key":"2193_CR9","doi-asserted-by":"crossref","unstructured":"Dang-Nguyen, D.T., Pasquini, C., Conotter, V., Boato, G.: Raise: a raw images dataset for digital image forensics. In: ACM Multimedia Systems Conference, pp. 219\u2013224. ACM (2015)","DOI":"10.1145\/2713168.2713194"},{"issue":"2","key":"2193_CR10","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2193_CR11","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: ECCV, pp. 391\u2013407 (2016)","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"2193_CR12","doi-asserted-by":"crossref","unstructured":"Fang, F., Li, J., Zeng, T.: Soft-edge assisted network for single image super-resolutionn. In: IEEE Transactions on Image Processing, vol.\u00a029, pp. 4656\u20134668. IEEE (2020)","DOI":"10.1109\/TIP.2020.2973769"},{"key":"2193_CR13","doi-asserted-by":"crossref","unstructured":"Freeman, I., Roese-Koerner, L., Kummert, A.: Effnet: An efficient structure for convolutional neural networks. In: IEEE International Conference on Image Processing (ICIP), pp. 6\u201310. IEEE (2018)","DOI":"10.1109\/ICIP.2018.8451339"},{"issue":"02","key":"2193_CR14","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/38.988747","volume":"22","author":"WT Freeman","year":"2002","unstructured":"Freeman, W.T., Pasztor, E.C., Jones, T.R.: Example-based super-resolution. IEEE Comp. Gr. Appl. 22(02), 56\u201365 (2002)","journal-title":"IEEE Comp. Gr. Appl."},{"key":"2193_CR15","doi-asserted-by":"crossref","unstructured":"Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: IEEE Conference on ICCV, pp. 349\u2013356. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"2193_CR16","doi-asserted-by":"crossref","unstructured":"Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: IEEE Conference on ICCV, pp. 349\u2013356 (2009)","DOI":"10.1109\/ICCV.2009.5459271"},{"key":"2193_CR17","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, J., Wang, J., Chen, Q., Cao, J., Deng, Z., Xu, Y., Tan, M.: Closed-loop matters: Dual regression networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00545"},{"key":"2193_CR18","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: IEEE conference on CVPR, pp. 1664\u20131673 (2018)","DOI":"10.1109\/CVPR.2018.00179"},{"key":"2193_CR19","doi-asserted-by":"crossref","unstructured":"Hayat, K.: Super-resolution via deep learning. arXiv preprint arXiv:1706.09077 (2017)","DOI":"10.1016\/j.dsp.2018.07.005"},{"key":"2193_CR20","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE conference on CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"2193_CR21","doi-asserted-by":"crossref","unstructured":"Hore, A., Ziou, D.: Image quality metrics: Psnr vs. ssim. In: International conference on Pattern recognition (icpr), pp. 2366\u20132369. IEEE (2010)","DOI":"10.1109\/ICPR.2010.579"},{"key":"2193_CR22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van\u00a0der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. IEEE Conference on CVPR pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2193_CR23","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: IEEE Conference on CVPR, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"2193_CR24","doi-asserted-by":"publisher","first-page":"2325","DOI":"10.1109\/TIP.2021.3050856","volume":"30","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Li, J., Gao, X., Hu, Y., Lu, W.: Interpretable detail-fidelity attention network for single image super-resolution. IEEE Trans. Image Process. 30, 2325\u20132339 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"2193_CR25","doi-asserted-by":"crossref","unstructured":"Hui, Z., Gao, X., Yang, Y., Wang, X.: Lightweight image super-resolution with information multi-distillation network. In: Proceedings of the 27th ACM International Conference on Multimedia (ACM MM), pp. 2024\u20132032 (2019)","DOI":"10.1145\/3343031.3351084"},{"key":"2193_CR26","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: IEEE Conference on CVPR, pp. 1646\u20131654 (2016)","DOI":"10.1109\/CVPR.2016.182"},{"key":"2193_CR27","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. IEEE Conference on CVPR pp. 1637\u20131645 (2016)","DOI":"10.1109\/CVPR.2016.181"},{"issue":"6","key":"2193_CR28","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1109\/TPAMI.2010.25","volume":"32","author":"KI Kim","year":"2010","unstructured":"Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1127\u20131133 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2193_CR29","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"2193_CR30","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: IEEE Conference on CVPR (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"2193_CR31","doi-asserted-by":"crossref","unstructured":"Lan, R., Sun, L., Liu, Z., Lu, H., Su, Z., Pang, C., Luo, X.: Cascading and enhanced residual networks for accurate single-image super-resolution. IEEE Transactions on Cybernetics pp. 1\u201311 (2020)","DOI":"10.1109\/TCYB.2019.2952710"},{"key":"2193_CR32","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et\u00a0al.: Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE conference on CVPR, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"key":"2193_CR33","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, F., Mei, K., Zhang, G.: Multi-scale residual network for image super-resolution. In: ECCV, pp. 517\u2013532 (2018)","DOI":"10.1007\/978-3-030-01237-3_32"},{"issue":"1","key":"2193_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3282445","volume":"15","author":"X Li","year":"2019","unstructured":"Li, X., Sun, Y., Yang, Y., Miao, C.: Symmetrical residual connections for single image super-resolution. ACM Tran. Multimed. Comput. Commun. Appl. (TOMM) 15(1), 1\u201310 (2019)","journal-title":"ACM Tran. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"2193_CR35","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. IEEE Conference on CVPR Workshops pp. 1132\u20131140 (2017)","DOI":"10.1109\/CVPRW.2017.151"},{"issue":"1","key":"2193_CR36","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/TPAMI.2004.1261081","volume":"26","author":"Z Lin","year":"2004","unstructured":"Lin, Z., Shum, H.Y.: Fundamental limits of reconstruction-based superresolution algorithms under local translation. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 83\u201397 (2004)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2193_CR37","doi-asserted-by":"crossref","unstructured":"Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: IEEE Conference on ICCV, vol.\u00a02, pp. 416\u2013423. IEEE (2001)","DOI":"10.1109\/ICCV.2001.937655"},{"issue":"6","key":"2193_CR38","first-page":"1423","volume":"25","author":"K Nasrollahi","year":"2014","unstructured":"Nasrollahi, K., Moeslund, T.B.: Super-resolution: a comprehensive survey. MVA 25(6), 1423\u20131468 (2014)","journal-title":"MVA"},{"key":"2193_CR39","doi-asserted-by":"crossref","unstructured":"Park, S.J., Son, H., Cho, S., Hong, K.S., Lee, S.: Srfeat: Single image super-resolution with feature discrimination. In: ECCV, pp. 439\u2013455 (2018)","DOI":"10.1007\/978-3-030-01270-0_27"},{"issue":"6","key":"2193_CR40","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1109\/TIP.2014.2305844","volume":"23","author":"T Peleg","year":"2014","unstructured":"Peleg, T., Elad, M.: A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans. Image Process. 23(6), 2569\u20132582 (2014)","journal-title":"IEEE Trans. Image Process."},{"key":"2193_CR41","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.procs.2020.04.015","volume":"171","author":"K Prajapati","year":"2020","unstructured":"Prajapati, K., Chudasama, V., Upla, K.: A light weight convolutional neural network for single image super-resolution. Proc. Comput. Sci. 171, 139\u2013147 (2020)","journal-title":"Proc. Comput. Sci."},{"key":"2193_CR42","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Sch\u00f6lkopf, B., Hirsch, M.: Enhancenet: Single image super-resolution through automated texture synthesis. In: IEEE Conference on ICCV, pp. 4501\u20134510. IEEE (2017)","DOI":"10.1109\/ICCV.2017.481"},{"key":"2193_CR43","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: IEEE Conference on CVPR, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"2193_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-01903-8","author":"W Shi","year":"2020","unstructured":"Shi, W., Du, H., Mei, W., Ma, Z.: (sarn)spatial-wise attention residual network for image super-resolution. Vis. Comput. (2020). https:\/\/doi.org\/10.1007\/s00371-020-01903-8","journal-title":"Vis. Comput."},{"key":"2193_CR45","doi-asserted-by":"crossref","unstructured":"Singh, A., Porikli, F., Ahuja, N.: Super-resolving noisy images. In: IEEE Conference on CVPR, pp. 2846\u20132853 (2014)","DOI":"10.1109\/CVPR.2014.364"},{"key":"2193_CR46","doi-asserted-by":"crossref","unstructured":"Soh, J.W., Park, G.Y., Jo, J., Cho, N.I.: Natural and realistic single image super-resolution with explicit natural manifold discrimination. In: IEEE Conference on CVPR, pp. 8122\u20138131 (2019)","DOI":"10.1109\/CVPR.2019.00831"},{"key":"2193_CR47","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., Liu, X.: Image super-resolution via deep recursive residual network. In: IEEE Conference on CVPR, vol.\u00a01 (2017)","DOI":"10.1109\/CVPR.2017.298"},{"key":"2193_CR48","doi-asserted-by":"crossref","unstructured":"Timofte, R., Agustsson, E., Van\u00a0Gool, L., Yang, M.H., Zhang, L.: Ntire 2017 challenge on single image super-resolution: Methods and results. In: IEEE Conference on CVPR Workshops, pp. 114\u2013125 (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"2193_CR49","doi-asserted-by":"crossref","unstructured":"Timofte, R., De, V., Van\u00a0Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: IEEE Conference on ICCV, pp. 1920\u20131927. IEEE (2013)","DOI":"10.1109\/ICCV.2013.241"},{"key":"2193_CR50","doi-asserted-by":"crossref","unstructured":"Tong, T., Li, G., Liu, X., Gao, Q.: Image super-resolution using dense skip connections. In: IEEE Conference on ICCV, pp. 4809\u20134817. IEEE (2017)","DOI":"10.1109\/ICCV.2017.514"},{"key":"2193_CR51","doi-asserted-by":"crossref","unstructured":"Trottier, L., Gigu, P., Chaib-draa, B., et\u00a0al.: Parametric exponential linear unit for deep convolutional neural networks. In: IEEE International Conference on Machine Learning and Applications, pp. 207\u2013214. IEEE (2017)","DOI":"10.1109\/ICMLA.2017.00038"},{"key":"2193_CR52","unstructured":"Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration. In: Advances in computer vision and image processing, pp. 317\u2013339 (1984)"},{"key":"2193_CR53","unstructured":"Wang, C., Li, Z., Shi, J.: Lightweight image super-resolution with adaptive weighted learning network. arXiv preprint arXiv:1904.02358 (2019)"},{"key":"2193_CR54","doi-asserted-by":"crossref","unstructured":"Wang, Y., Perazzi, F., McWilliams, B., Sorkine-Hornung, A., Sorkine-Hornung, O., Schroers, C.: A fully progressive approach to single-image super-resolution. In: IEEE Conference on CVPR Workshops (2018)","DOI":"10.1109\/CVPRW.2018.00131"},{"issue":"4","key":"2193_CR55","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(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"2193_CR56","doi-asserted-by":"crossref","unstructured":"Yang, C.Y., Ma, C., Yang, M.H.: Single-image super-resolution: A benchmark. In: ECCV, pp. 372\u2013386. Springer (2014)","DOI":"10.1007\/978-3-319-10593-2_25"},{"key":"2193_CR57","doi-asserted-by":"crossref","unstructured":"Yang, J., Lin, Z., Cohen, S.: Fast image super-resolution based on in-place example regression. In: IEEE Conference on CVPR, pp. 1059\u20131066. IEEE (2013)","DOI":"10.1109\/CVPR.2013.141"},{"key":"2193_CR58","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.H., Liao, Q.: Deep learning for single image super-resolution: A brief review. IEEE Trans. Multimed. (2019)","DOI":"10.1109\/TMM.2019.2919431"},{"key":"2193_CR59","doi-asserted-by":"crossref","unstructured":"Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: International conference on curves and surfaces, pp. 711\u2013730. Springer (2010)","DOI":"10.1007\/978-3-642-27413-8_47"},{"issue":"3","key":"2193_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3398685","volume":"16","author":"D Zhang","year":"2020","unstructured":"Zhang, D., Shao, J., Shen, H.T.: Kernel attention network for single image super-resolution. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(3), 1\u201315 (2020)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"2193_CR61","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: ECCV, pp. 286\u2013301 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"2193_CR62","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image super-resolution. In: IEEE Conference on CVPR, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"}],"container-title":["The Visual Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02193-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00371-021-02193-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00371-021-02193-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T11:02:07Z","timestamp":1725361327000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00371-021-02193-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":62,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["2193"],"URL":"https:\/\/doi.org\/10.1007\/s00371-021-02193-4","relation":{},"ISSN":["0178-2789","1432-2315"],"issn-type":[{"type":"print","value":"0178-2789"},{"type":"electronic","value":"1432-2315"}],"subject":[],"published":{"date-parts":[[2021,7,5]]},"assertion":[{"value":"3 June 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2021","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 interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Yes ()","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}