{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:20:29Z","timestamp":1775913629409,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"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":["61601130"],"award-info":[{"award-number":["61601130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Planning Project of Daya Bay","award":["2020010203"],"award-info":[{"award-number":["2020010203"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18677-z","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T06:02:31Z","timestamp":1709272951000},"page":"79805-79814","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-scale cross-fusion for arbitrary scale image super resolution"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8048-6674","authenticated-orcid":false,"given":"Guangping","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanling","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingkai","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingo Wing-Kuen","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"issue":"2","key":"18677_CR1","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2015","unstructured":"Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295\u2013307","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"18677_CR2","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"key":"18677_CR3","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1637\u20131645","DOI":"10.1109\/CVPR.2016.181"},{"key":"18677_CR4","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"},{"key":"18677_CR5","doi-asserted-by":"crossref","unstructured":"Tong T, Li G, Liu X, Gao Q (2017) Image super-resolution using dense skip connections. In: Proceedings of the IEEE international conference on computer vision, pp 4799\u20134807","DOI":"10.1109\/ICCV.2017.514"},{"key":"18677_CR6","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, Wang L, Zhong B, Fu Y (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European conference on computer vision (ECCV), pp 286\u2013301","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"18677_CR7","doi-asserted-by":"crossref","unstructured":"Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding","DOI":"10.5244\/C.26.135"},{"key":"18677_CR8","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1646\u20131654","DOI":"10.1109\/CVPR.2016.182"},{"key":"18677_CR9","doi-asserted-by":"crossref","unstructured":"Lai W-S, Huang J-B, Ahuja N, Yang M-H (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 624\u2013632","DOI":"10.1109\/CVPR.2017.618"},{"key":"18677_CR10","doi-asserted-by":"crossref","unstructured":"Kim J-H, Lee J-S (2018) Deep residual network with enhanced upscaling module for super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 800\u2013808","DOI":"10.1109\/CVPRW.2018.00124"},{"key":"18677_CR11","doi-asserted-by":"crossref","unstructured":"Hui Z, Wang X, Gao X (2018) Fast and accurate single image super-resolution via information distillation network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 723\u2013731","DOI":"10.1109\/CVPR.2018.00082"},{"key":"18677_CR12","doi-asserted-by":"crossref","unstructured":"Shuai Y, Wang Y, Peng Y, Xia Y (2018) Accurate image super-resolution using cascaded multi-column convolutional neural networks. In: 2018 IEEE International conference on multimedia and expo (ICME), IEEE, pp 1\u20136","DOI":"10.1109\/ICME.2018.8486509"},{"key":"18677_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2020.107567","volume":"172","author":"K Chang","year":"2020","unstructured":"Chang K, Li M, Ding PLK, Li B (2020) Accurate single image super-resolution using multi-path wide-activated residual network. Signal Process 172:107567","journal-title":"Signal Process"},{"key":"18677_CR14","doi-asserted-by":"crossref","unstructured":"Li F, Cong R, Bai H, He Y (2020) Deep interleaved network for image super-resolution with asymmetric co-attention. arXiv preprint arXiv:2004.11814","DOI":"10.24963\/ijcai.2020\/75"},{"key":"18677_CR15","doi-asserted-by":"crossref","unstructured":"Huang Y, Shao L, Frangi AF (2017) Simultaneous super-resolution and cross-modality synthesis of 3d medical images using weakly-supervised joint convolutional sparse coding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6070\u20136079","DOI":"10.1109\/CVPR.2017.613"},{"key":"18677_CR16","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.compmedimag.2018.10.005","volume":"71","author":"D Mahapatra","year":"2019","unstructured":"Mahapatra D, Bozorgtabar B, Garnavi R (2019) Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput Med Imaging Graph 71:30\u201339","journal-title":"Comput Med Imaging Graph"},{"issue":"13","key":"18677_CR17","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.3390\/rs11131588","volume":"11","author":"T Lu","year":"2019","unstructured":"Lu T, Wang J, Zhang Y, Wang Z, Jiang J (2019) Satellite image super-resolution via multi-scale residual deep neural network. Remote Sensing 11(13):1588","journal-title":"Remote Sensing"},{"key":"18677_CR18","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu\u00a0Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 136\u2013144","DOI":"10.1109\/CVPRW.2017.151"},{"key":"18677_CR19","doi-asserted-by":"crossref","unstructured":"Hu X, Mu H, Zhang X, Wang Z, Tan T, Sun J (2019) Meta-sr: A magnification-arbitrary network for super-resolution. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1575\u20131584","DOI":"10.1109\/CVPR.2019.00167"},{"key":"18677_CR20","doi-asserted-by":"crossref","unstructured":"Chen Y, Liu S, Wang X (2021) Learning continuous image representation with local implicit image function. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8628\u20138638","DOI":"10.1109\/CVPR46437.2021.00852"},{"key":"18677_CR21","doi-asserted-by":"crossref","unstructured":"Lee J, Jin KH (2022) Local texture estimator for implicit representation function. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1929\u20131938","DOI":"10.1109\/CVPR52688.2022.00197"},{"key":"18677_CR22","doi-asserted-by":"crossref","unstructured":"Wang L, Wang Y, Lin Z, Yang J, An W, Guo Y (2021) Learning a single network for scale-arbitrary super-resolution. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 4801\u20134810","DOI":"10.1109\/ICCV48922.2021.00476"},{"key":"18677_CR23","doi-asserted-by":"crossref","unstructured":"Li G, Xiao H, Liang D (2022) Enhanced dual branches network for arbitrary-scale image super-resolution. Electron Lett","DOI":"10.2139\/ssrn.4330134"},{"key":"18677_CR24","first-page":"1","volume":"61","author":"L Li","year":"2023","unstructured":"Li L, Han L, Ding M, Cao H (2023) Multimodal image fusion framework for end-to-end remote sensing image registration. IEEE Trans Geosci Remote Sens 61:1\u201314","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"18677_CR25","doi-asserted-by":"crossref","unstructured":"Zeyde R, Elad M, Protter M (2010) On single image scale-up using sparse-representations. In: international conference on curves and surfaces, Springer, pp 711\u2013730","DOI":"10.1007\/978-3-642-27413-8_47"},{"key":"18677_CR26","doi-asserted-by":"crossref","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, IEEE, 2:416\u2013423","DOI":"10.1109\/ICCV.2001.937655"},{"key":"18677_CR27","doi-asserted-by":"crossref","unstructured":"Huang J-B, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5197\u20135206","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"18677_CR28","unstructured":"Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18677-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18677-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18677-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T13:29:09Z","timestamp":1728307749000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18677-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,1]]},"references-count":28,"journal-issue":{"issue":"33","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["18677"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18677-z","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,1]]},"assertion":[{"value":"24 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2024","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\u2019 research does not address ethical issues.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"The authors declared no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}