{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T06:36:38Z","timestamp":1792823798512,"version":"4.5.0"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Sichuan Science and Technology Program","award":["2019YJ0181"],"award-info":[{"award-number":["2019YJ0181"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"crossref","award":["2016YFC0100800"],"award-info":[{"award-number":["2016YFC0100800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"crossref","award":["2016YFC0100802"],"award-info":[{"award-number":["2016YFC0100802"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1109\/tip.2019.2921882","type":"journal-article","created":{"date-parts":[[2019,6,14]],"date-time":"2019-06-14T16:04:31Z","timestamp":1560528271000},"page":"5649-5662","source":"Crossref","is-referenced-by-count":181,"title":["Channel Splitting Network for Single MR Image Super-Resolution"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0100-2414","authenticated-orcid":false,"given":"Xiaole","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2288-5079","authenticated-orcid":false,"given":"Yulun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueming","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2005.09.011"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24187"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1117\/12.2243617"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40760-4_2"},{"key":"ref5","article-title":"Aerial spectral super-resolution using conditional adversarial networks","author":"Rangnekar","year":"2017","journal-title":"arXiv:1712.08690"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1080\/01431160500207088"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2012.2230270"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.4304\/jcm.7.6.464-472"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1137\/s0036144596301390"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/1049-9652(91)90045-L"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1364\/JOSAA.6.001715"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/38.988747"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2013.2249514"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587647"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2192127"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_25"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.486"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950500"},{"key":"ref25","article-title":"Brain MRI super resolution using 3D deep densely connected neural networks","author":"Chen","year":"2018","journal-title":"arXiv:1801.02728"},{"key":"ref26","article-title":"Self super-resolution for magnetic resonance images using deep networks","author":"Zhao","year":"2018","journal-title":"arXiv:1802.09431"},{"key":"ref27","article-title":"Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network","author":"Chen","year":"2018","journal-title":"arXiv:1803.01417"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2843819"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aab9e9"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref32","article-title":"Residual dense network for image super-resolution","author":"Zhang","year":"2018","journal-title":"arXiv:1802.08797"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref35","article-title":"Densely connected convolutional networks","author":"Huang","year":"2016","journal-title":"arXiv:1608.06993"},{"key":"ref36","article-title":"Deep convolutional neural networks with merge-and-run mappings","author":"Zhao","year":"2017","journal-title":"arXiv:1611.07718"},{"key":"ref37","article-title":"Single image super-resolution via cascaded multi-scale cross network","author":"Hu","year":"2018","journal-title":"arXiv:1802.08808"},{"key":"ref38","article-title":"Inception-v4, inception-ResNet and the impact of residual connections on learning","author":"Szegedy","year":"2016","journal-title":"arXiv:1602.07261"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s10825-006-0145-z"},{"key":"ref40","article-title":"Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network","author":"Shi","year":"2016","journal-title":"arXiv:1609.05158"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1002\/1522-2594(200101)45:1<29::AID-MRM1005>3.0.CO;2-Z"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/S0730-725X(02)00511-8"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.2007348"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88682-2_38"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1155\/2010\/425891"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2012.09.003"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2013.10.026"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/IranianCEE.2012.6292620"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46726-9_29"},{"key":"ref50","article-title":"End-to-end image super-resolution via deep and shallow convolutional networks","author":"Wang","year":"2016","journal-title":"arXiv:1607.07680"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.142"},{"key":"ref52","article-title":"Fast and accurate image super resolution by deep CNN with skip connection and network in network","author":"Yamanaka","year":"2017","journal-title":"arXiv:1707.05425"},{"key":"ref53","article-title":"Dual path networks","author":"Chen","year":"2017","journal-title":"arXiv:1707.01629"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref55","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980v9"},{"key":"ref56","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume-title":"Proc. AISTATS","volume":"9","author":"Glorot"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2010.05.010"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_16"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/8809862\/08736987.pdf?arnumber=8736987","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T05:36:57Z","timestamp":1792820217000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8736987\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11]]},"references-count":58,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tip.2019.2921882","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11]]}}}