{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:05:59Z","timestamp":1760123159198,"version":"3.37.3"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T00:00:00Z","timestamp":1644969600000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11042-022-11970-9","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T06:03:54Z","timestamp":1644991434000},"page":"10607-10630","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Single image super-resolution with self-organization neural networks and image laplace gradient operator"],"prefix":"10.1007","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3594-2762","authenticated-orcid":false,"given":"Khodabakhsh","family":"Ahmadian","sequence":"first","affiliation":[]},{"given":"Hamid-reza","family":"Reza-Alikhani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,16]]},"reference":[{"key":"11970_CR1","doi-asserted-by":"crossref","unstructured":"Ahn N, Kang B, Sohn K-A (2018) Fast, accurate, and lightweight super-resolution with cascading residual network","DOI":"10.1109\/CVPRW.2018.00123"},{"issue":"3","key":"11970_CR2","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","volume":"46","author":"NS Altman","year":"1992","unstructured":"Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Amer Stat 46(3):175\u2013185","journal-title":"Amer Stat"},{"key":"11970_CR3","doi-asserted-by":"crossref","unstructured":"Andreas R, Dieter M (1999) Automatic labeling of self-organizing maps: Making a treasure-map reveal its secrets. In: Ning Z, Lizhu Z (eds) Methodologies for Knowledge Discovery and Data Mining. Springer, Berlin, pp 228\u2013237","DOI":"10.1007\/3-540-48912-6_31"},{"key":"11970_CR4","doi-asserted-by":"publisher","unstructured":"Burger HC, Schuler CJ, Harmeling S (2012) Image denoising: Can plain neural networks compete with bm3d?. In: 2012 IEEE conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2012.6247952, pp 2392\u20132399","DOI":"10.1109\/CVPR.2012.6247952"},{"key":"11970_CR5","doi-asserted-by":"publisher","first-page":"102224","DOI":"10.1016\/j.bspc.2020.102224","volume":"64","author":"S Ding","year":"2021","unstructured":"Ding S, Zheng J, Liu Z, Zheng Y, Chen Y, Xu X, Lu J, Xie J (2021) High-resolution dermoscopy image synthesis with conditional generative adversarial networks. Biomed Signal Process Control 64:102224. https:\/\/doi.org\/10.1016\/j.bspc.2020.102224","journal-title":"Biomed Signal Process Control"},{"key":"11970_CR6","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"11970_CR7","doi-asserted-by":"crossref","unstructured":"Dong C, Loy CC, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: European conference on computer vision. Springer, pp 391\u2013407","DOI":"10.1007\/978-3-319-46475-6_25"},{"issue":"10","key":"11970_CR8","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.1109\/TIP.2004.834669","volume":"13","author":"S Farsiu","year":"2004","unstructured":"Farsiu S, Robinson MD, Elad M, Milanfar P (2004) Fast and robust multiframe super resolution. IEEE Trans Image Process 13(10):1327\u20131344. https:\/\/doi.org\/10.1109\/TIP.2004.834669","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"11970_CR9","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/38.988747","volume":"22","author":"WT Freeman","year":"2002","unstructured":"Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56\u201365. https:\/\/doi.org\/10.1109\/38.988747","journal-title":"IEEE Comput Graph Appl"},{"issue":"12","key":"11970_CR10","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1109\/83.650116","volume":"6","author":"RC Hardie","year":"1997","unstructured":"Hardie RC, Barnard KJ, Armstrong EE (1997) Joint map registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans Image Process 6(12):1621\u20131633. https:\/\/doi.org\/10.1109\/83.650116","journal-title":"IEEE Trans Image Process"},{"issue":"2004","key":"11970_CR11","first-page":"41","volume":"2","author":"S Haykin","year":"2004","unstructured":"Haykin S, Network N (2004) A comprehensive foundation. Neural Netw 2(2004):41","journal-title":"Neural Netw"},{"issue":"6","key":"11970_CR12","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TASSP.1978.1163154","volume":"26","author":"H Hou","year":"1978","unstructured":"Hou H, Andrews H (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Process 26(6):508\u2013517. https:\/\/doi.org\/10.1109\/TASSP.1978.1163154","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"11970_CR13","doi-asserted-by":"publisher","unstructured":"Huang J, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2015.7299156, pp 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"11970_CR14","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.neucom.2020.05.008","volume":"404","author":"Z Hui","year":"2020","unstructured":"Hui Z, Gao X, Wang X (2020) Lightweight image super-resolution with feature enhancement residual network. Neurocomputing 404:50\u201360. https:\/\/doi.org\/10.1016\/j.neucom.2020.05.008","journal-title":"Neurocomputing"},{"key":"11970_CR15","unstructured":"Ignatov A, Timofte R, et al (2019) Pirm challenge on perceptual image enhancement on smartphones: report. In: European Conference on Computer Vision (ECCV) Workshops"},{"key":"11970_CR16","doi-asserted-by":"publisher","first-page":"101868","DOI":"10.1016\/j.bspc.2020.101868","volume":"58","author":"M Jalali","year":"2020","unstructured":"Jalali M, Behnam H, Davoodi F, Shojaeifard M (2020) Temporal super-resolution of 2d\/3d echocardiography using cubic b-spline interpolation. Biomed Signal Process Control 58:101868. https:\/\/doi.org\/10.1016\/j.bspc.2020.101868","journal-title":"Biomed Signal Process Control"},{"key":"11970_CR17","doi-asserted-by":"publisher","first-page":"107475","DOI":"10.1016\/j.patcog.2020.107475","volume":"107","author":"K Jiang","year":"2020","unstructured":"Jiang K, Wang Z, Yi P, Jiang J (2020) Hierarchical dense recursive network for image super-resolution. Pattern Recogn 107:107475. https:\/\/doi.org\/10.1016\/j.patcog.2020.107475","journal-title":"Pattern Recogn"},{"key":"11970_CR18","doi-asserted-by":"publisher","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2016.182, pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"11970_CR19","doi-asserted-by":"publisher","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2016.181, pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"issue":"6","key":"11970_CR20","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1109\/TPAMI.2010.25","volume":"32","author":"KI Kim","year":"2010","unstructured":"Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 32(6):1127\u20131133. https:\/\/doi.org\/10.1109\/TPAMI.2010.25","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"6","key":"11970_CR21","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"11970_CR22","doi-asserted-by":"publisher","unstructured":"Lai W-S, Huang J-B, Ahuja N, Yang M-H (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2017.618, pp 5835\u20135843","DOI":"10.1109\/CVPR.2017.618"},{"key":"11970_CR23","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network, pp 105\u2013114","DOI":"10.1109\/CVPR.2017.19"},{"issue":"10","key":"11970_CR24","doi-asserted-by":"publisher","first-page":"1521","DOI":"10.1109\/83.951537","volume":"10","author":"X Li","year":"2001","unstructured":"Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521\u20131527. https:\/\/doi.org\/10.1109\/83.951537","journal-title":"IEEE Trans Image Process"},{"key":"11970_CR25","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Lee K (2017) Enhanced deep residual networks for single image super-resolution, pp 1132\u20131140","DOI":"10.1109\/CVPRW.2017.151"},{"key":"11970_CR26","doi-asserted-by":"publisher","first-page":"102085","DOI":"10.1016\/j.bspc.2020.102085","volume":"62","author":"H Liu","year":"2020","unstructured":"Liu H, Lin Y, Ibragimov B, Zhang C (2020) Low dose 4d-ct super-resolution reconstruction via inter-plane motion estimation based on optical flow. Biomed Signal Process Control 62:102085. https:\/\/doi.org\/10.1016\/j.bspc.2020.102085","journal-title":"Biomed Signal Process Control"},{"key":"11970_CR27","doi-asserted-by":"publisher","first-page":"108184","DOI":"10.1016\/j.sigpro.2021.108184","volume":"188","author":"J Liu","year":"2021","unstructured":"Liu J, Liu Y, Wu H, Wang J, Li X, Zhang C (2021) Single image super-resolution using feature adaptive learning and global structure sparsity. Signal Process 188:108184. https:\/\/doi.org\/10.1016\/j.sigpro.2021.108184, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S016516842100222X","journal-title":"Signal Process"},{"key":"11970_CR28","doi-asserted-by":"crossref","unstructured":"Macwan R, Patel N, Prajapati P, Chavda J (2014) A survey on various techniques of super resolution imaging. Int J Comput Appl 90(1)","DOI":"10.5120\/15539-4214"},{"key":"11970_CR29","unstructured":"Mao X-J, Shen C, Yang Y-B (2016) Image denoising using very deep fully convolutional encoder-decoder networks with symmetric skip connections"},{"key":"11970_CR30","doi-asserted-by":"publisher","unstructured":"Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE international conference on computer vision. ICCV 2001. https:\/\/doi.org\/10.1109\/ICCV.2001.937655, vol 2, pp 416\u2013423","DOI":"10.1109\/ICCV.2001.937655"},{"key":"11970_CR31","doi-asserted-by":"publisher","unstructured":"Matsui Y, Ito K, Aramaki Y, Yamasaki T, Aizawa K (2017) Sketch-based manga retrieval using manga109 dataset. Multimed Tools Appl 76. https:\/\/doi.org\/10.1007\/s11042-016-4020-z","DOI":"10.1007\/s11042-016-4020-z"},{"issue":"6","key":"11970_CR32","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1007\/s00138-014-0623-4","volume":"25","author":"K Nasrollahi","year":"2014","unstructured":"Nasrollahi K, Moeslund TB (2014) Super-resolution: a comprehensive survey. Mach Vis Appl 25(6):1423\u20131468","journal-title":"Mach Vis Appl"},{"key":"11970_CR33","unstructured":"Padraig C, Sarah D (2007) k-nearest neighbour classifiers. Mult Classif Syst"},{"issue":"6","key":"11970_CR34","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 (2014) A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans Image Process 23(6):2569\u20132582. https:\/\/doi.org\/10.1109\/TIP.2014.2305844","journal-title":"IEEE Trans Image Process"},{"key":"11970_CR35","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein MS, Berg A, Fei-Fei L (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252","journal-title":"Int J Comput Vis"},{"key":"11970_CR36","doi-asserted-by":"publisher","unstructured":"Schuler CJ, Burger HC, Harmeling S, Sch\u00f6lkopf B (2013) A machine learning approach for non-blind image deconvolution. In: 2013 IEEE conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2013.142, pp 1067\u20131074","DOI":"10.1109\/CVPR.2013.142"},{"key":"11970_CR37","doi-asserted-by":"publisher","unstructured":"Schulter S, Leistner C, Bischof H (2015) Fast and accurate image upscaling with super-resolution forests. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2015.7299003, pp 3791\u20133799","DOI":"10.1109\/CVPR.2015.7299003"},{"key":"11970_CR38","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken A, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network","DOI":"10.1109\/CVPR.2016.207"},{"key":"11970_CR39","doi-asserted-by":"publisher","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). https:\/\/doi.org\/10.1109\/CVPR.2017.298, pp 2790\u20132798","DOI":"10.1109\/CVPR.2017.298"},{"key":"11970_CR40","doi-asserted-by":"publisher","unstructured":"Tai Y, Yang J, Liu X, Xu C (2017) Memnet: A persistent memory network for image restoration. In: 2017 IEEE international conference on computer vision (ICCV). https:\/\/doi.org\/10.1109\/ICCV.2017.486, pp 4549\u20134557","DOI":"10.1109\/ICCV.2017.486"},{"key":"11970_CR41","volume-title":"Matlab implementations and applications of the self-organizing map","author":"K Teuvo","year":"2014","unstructured":"Teuvo K (2014) Matlab implementations and applications of the self-organizing map, 1st edn. Unigrafia Oy, Helsinki","edition":"1st edn."},{"key":"11970_CR42","doi-asserted-by":"publisher","unstructured":"Tian C, Xu Y, Zuo W, Zhang B, Fei L, Lin C-W (2020) Coarse-to-fine cnn for image super-resolution. IEEE Trans Multimed:1\u20131. https:\/\/doi.org\/10.1109\/TMM.2020.2999182","DOI":"10.1109\/TMM.2020.2999182"},{"key":"11970_CR43","doi-asserted-by":"publisher","first-page":"106235","DOI":"10.1016\/j.knosys.2020.106235","volume":"205","author":"C Tian","year":"2020","unstructured":"Tian C, Zhuge R, Wu Z, Xu Y, Zuo W, Chen C, Lin C-W (2020) Lightweight image super-resolution with enhanced cnn. Knowl-Based Syst 205:106235. https:\/\/doi.org\/10.1016\/j.knosys.2020.106235","journal-title":"Knowl-Based Syst"},{"key":"11970_CR44","doi-asserted-by":"publisher","unstructured":"Timofte R, De V, Gool LV (2013) Anchored neighborhood regression for fast example-based super-resolution. In: 2013 IEEE international conference on computer vision. https:\/\/doi.org\/10.1109\/ICCV.2013.241, pp 1920\u20131927","DOI":"10.1109\/ICCV.2013.241"},{"key":"11970_CR45","doi-asserted-by":"publisher","unstructured":"Timofte R, De Smet V, Van Gool L (2015) A+: Adjusted anchored neighborhood regression for fast super-resolution. 111\u2013126. https:\/\/doi.org\/10.1007\/978-3-319-16817-3_8","DOI":"10.1007\/978-3-319-16817-3_8"},{"key":"11970_CR46","doi-asserted-by":"publisher","unstructured":"Tong T, Li G, Liu X, Gao Q (2017) Image super-resolution using dense skip connections. In: 2017 IEEE International Conference on Computer Vision (ICCV). https:\/\/doi.org\/10.1109\/ICCV.2017.514, pp 4809\u20134817","DOI":"10.1109\/ICCV.2017.514"},{"key":"11970_CR47","unstructured":"Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) Self-organizing map in matlab: The som toolbox. Proceedings of the Proceedings of the Matlab DSP Conference, vol 99"},{"issue":"5","key":"11970_CR48","doi-asserted-by":"publisher","first-page":"886","DOI":"10.1109\/TPAMI.2007.1027","volume":"29","author":"X Wang","year":"2007","unstructured":"Wang X (2007) Laplacian operator-based edge detectors. IEEE Trans Pattern Anal Mach Intell 29(5):886\u2013890. https:\/\/doi.org\/10.1109\/TPAMI.2007.1027","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11970_CR49","doi-asserted-by":"publisher","unstructured":"Wang Z, Liu D, Yang J, Han W, Huang T (2015) Deep networks for image super-resolution with sparse prior. In: 2015 IEEE international conference on computer vision (ICCV). https:\/\/doi.org\/10.1109\/ICCV.2015.50, pp 370\u2013378","DOI":"10.1109\/ICCV.2015.50"},{"issue":"11","key":"11970_CR50","doi-asserted-by":"publisher","first-page":"5369","DOI":"10.1109\/TIP.2016.2604489","volume":"25","author":"J Wu","year":"2016","unstructured":"Wu J, Anisetti M, Wu W, Damiani E, Jeon G (2016) Bayer demosaicking with polynomial interpolation. IEEE Trans Image Process 25(11):5369\u20135382. https:\/\/doi.org\/10.1109\/TIP.2016.2604489","journal-title":"IEEE Trans Image Process"},{"key":"11970_CR51","doi-asserted-by":"crossref","unstructured":"Yang F, Xu W, Tian Y (2017) Image super resolution using deep convolutional network based on topology aggregation structure. In: AIP Conference Proceedings, vol 1864. AIP Publishing LLC, p 020185","DOI":"10.1063\/1.4993002"},{"issue":"11","key":"11970_CR52","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","volume":"19","author":"J Yang","year":"2010","unstructured":"Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861\u20132873. https:\/\/doi.org\/10.1109\/TIP.2010.2050625","journal-title":"IEEE Trans Image Process"},{"key":"11970_CR53","doi-asserted-by":"publisher","unstructured":"Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. 711\u2013730. https:\/\/doi.org\/10.1007\/978-3-642-27413-8_47","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"11970_CR54","doi-asserted-by":"publisher","unstructured":"Zheng H, Qu X, Bai Z, Liu Y, Dong J, Peng X, Chen Z (2017) Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity. BMC Med Imaging 17. https:\/\/doi.org\/10.1186\/s12880-016-0176-2","DOI":"10.1186\/s12880-016-0176-2"},{"key":"11970_CR55","doi-asserted-by":"publisher","unstructured":"Zheng H, Qu X, Bai Z, Liu Y, Dong J, Peng X, Chen Z (2017) Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity. BMC Med Imaging 17. https:\/\/doi.org\/10.1186\/s12880-016-0176-2","DOI":"10.1186\/s12880-016-0176-2"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-11970-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-11970-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-11970-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T18:00:33Z","timestamp":1726682433000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-11970-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,16]]},"references-count":55,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["11970"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-11970-9","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,2,16]]},"assertion":[{"value":"12 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2022","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 they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}