{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:19:29Z","timestamp":1740169169819,"version":"3.37.3"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876037"],"award-info":[{"award-number":["61876037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State\u2019s Key Project of Research"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2978084","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T21:49:26Z","timestamp":1583272166000},"page":"44867-44878","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Extraction of Blur Regions on a Single Image Based on Semantic Segmentation"],"prefix":"10.1109","volume":"8","author":[{"given":"Aodong","family":"Shen","sequence":"first","affiliation":[]},{"given":"Han","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2095-8470","authenticated-orcid":false,"given":"Youyong","family":"Kong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7171-1318","authenticated-orcid":false,"given":"Jiasong","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3833-7915","authenticated-orcid":false,"given":"Huazhong","family":"Shu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref38","article-title":"Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery","author":"sherrah","year":"2016","journal-title":"arXiv 1606 02585"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1109\/ICCV.2015.178"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref31","article-title":"Aggregated residual transformations for deep neural networks","author":"xie","year":"2016","journal-title":"arXiv 1611 05431"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.5244\/C.30.87"},{"key":"ref37","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref36","article-title":"SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling","author":"badrinarayanan","year":"0","journal-title":"Comput Sci"},{"key":"ref35","doi-asserted-by":"crossref","DOI":"10.1023\/A:1011130501691","article-title":"ParseNet: Looking wider to see better","author":"liu","year":"0","journal-title":"Comput Sci"},{"key":"ref34","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1007\/978-3-319-46478-7_46"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1109\/CVPR.2015.7298677"},{"key":"ref29","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/CVPR.2014.379"},{"year":"2009","author":"biemond","journal-title":"Basic Methods for Image Restoration and Identification","key":"ref1"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1109\/CVPR.2015.7298665"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1007\/978-3-319-64698-5_23"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/TIP.2014.2362055"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/TPAMI.2013.18"},{"key":"ref23","first-page":"1033","article-title":"Fast image deconvolution using hyper-Laplacian priors","author":"krishnan","year":"2009","journal-title":"Proc Adv Neural Inf Process Syst"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/TIP.2016.2535273"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/TPAMI.2012.213"},{"doi-asserted-by":"publisher","key":"ref50","DOI":"10.1134\/S1054661818030082"},{"doi-asserted-by":"publisher","key":"ref51","DOI":"10.1109\/CVPR.2018.00325"},{"doi-asserted-by":"publisher","key":"ref53","DOI":"10.1109\/TIP.2016.2528042"},{"doi-asserted-by":"publisher","key":"ref52","DOI":"10.1111\/cgf.13567"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/TNNLS.2016.2522401"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1109\/TFUZZ.2016.2574915"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref13","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"0","journal-title":"Comput Sci"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/CVPR.2015.7298594"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/CVPR.2016.90"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/CVPR.2015.7298965"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref18","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1145\/2072298.2072024"},{"key":"ref4","first-page":"1","article-title":"Image partial blur detection and classification","author":"liu","year":"2008","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1109\/TCYB.2015.2472478"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1016\/j.patcog.2011.03.009"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/CVPR.2010.5539954"},{"key":"ref8","first-page":"46","article-title":"A review on estimation of defocus blur from a single image","volume":"106","author":"tiwari","year":"2014","journal-title":"Int J Comput Appl"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1016\/j.jvcir.2016.01.002"},{"doi-asserted-by":"publisher","key":"ref49","DOI":"10.1109\/TIP.2007.899601"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1364\/OL.38.001706"},{"key":"ref46","first-page":"1","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Proc 29th Annu Conf Neural Inf Process Syst (NIPS)"},{"doi-asserted-by":"publisher","key":"ref45","DOI":"10.1109\/LSP.2014.2364612"},{"doi-asserted-by":"publisher","key":"ref48","DOI":"10.1109\/CVPR.2017.71"},{"doi-asserted-by":"publisher","key":"ref47","DOI":"10.1109\/TPAMI.2018.2815688"},{"doi-asserted-by":"publisher","key":"ref42","DOI":"10.1145\/2647868.2654889"},{"key":"ref41","article-title":"What makes ImageNet good for transfer learning","author":"huh","year":"2016","journal-title":"arXiv 1608 08614"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"ref43","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Statist"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09022961.pdf?arnumber=9022961","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T01:09:18Z","timestamp":1641949758000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9022961\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2978084","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2020]]}}}