{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T12:25:17Z","timestamp":1790857517135,"version":"4.1.0"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"ARC Discovery Project","award":["DP160100703"],"award-info":[{"award-number":["DP160100703"]}]},{"name":"ARC Discovery Project","award":["DP200103797"],"award-info":[{"award-number":["DP200103797"]}]},{"name":"Chang Jiang Scholars Program of China","award":["100017GH030150"],"award-info":[{"award-number":["100017GH030150"]}]},{"name":"Chang Jiang Scholars Program of China","award":["15GH0301"],"award-info":[{"award-number":["15GH0301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1109\/tnnls.2020.2968289","type":"journal-article","created":{"date-parts":[[2020,2,18]],"date-time":"2020-02-18T08:04:21Z","timestamp":1582013061000},"page":"5468-5482","source":"Crossref","is-referenced-by-count":112,"title":["Learning Deep Gradient Descent Optimization for Image Deconvolution"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2668-9630","authenticated-orcid":false,"given":"Dong","family":"Gong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinfeng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3027-8364","authenticated-orcid":false,"given":"Anton","family":"van den Hengel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8648-8718","authenticated-orcid":false,"given":"Chunhua","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2977-8057","authenticated-orcid":false,"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1934","article-title":"MPGL: An efficient matching pursuit method for generalized lasso","author":"gong","year":"2017","journal-title":"Proc AAAI"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1561\/2400000003"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.83"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8545043"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPHOT.2016.7492871"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/978-3-319-66709-6_23","article-title":"Variational networks: Connecting variational methods and deep learning","author":"kobler","year":"2017","journal-title":"Proc German Conf Pattern Recognit"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_34"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.435"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.34"},{"key":"ref28","article-title":"Optimization as a model for few-shot learning","author":"ravi","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref27","first-page":"3981","article-title":"Learning to learn by gradient descent by gradient descent","author":"andrychowicz","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref2","first-page":"157","article-title":"Two-phase kernel estimation for robust motion deblurring","author":"xu","year":"2010","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.371"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2441053"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6737048"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299163"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/83.392335"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/2897824.2925875","article-title":"Proximal: Efficient image optimization using proximal algorithms","volume":"35","author":"heide","year":"2016","journal-title":"ACM Trans Graph"},{"key":"ref26","article-title":"Learning to optimize","author":"li","year":"2016","journal-title":"arXiv 1606 01885"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.408"},{"key":"ref50","first-page":"1","article-title":"Super-resolution from Internet-scale scene matching","author":"sun","year":"2012","journal-title":"Proc IEEE Int Conf Comput Photography (ICCP)"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/EUSIPCO.2015.7362905"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.160"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.184"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.202"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref52","first-page":"227","article-title":"An introduction to total variation for image analysis","volume":"9","author":"chambolle","year":"2010","journal-title":"Theoretical Foundations and Numerical Methods for Sparse Recovery"},{"key":"ref10","first-page":"1790","article-title":"Deep convolutional neural network for image deconvolution","author":"xu","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.737"},{"key":"ref40","first-page":"7","volume":"35","author":"wright","year":"1999","journal-title":"Numerical Optimization"},{"key":"ref12","first-page":"5888","article-title":"One network to solve them all&#x2014;Solving linear inverse problems using deep projection models","author":"chang","year":"2017","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.300"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2862631"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.491"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539844"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2875352"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1137\/120896219"},{"key":"ref19","first-page":"231","article-title":"Good image priors for non-blind deconvolution: Generic vs specific","author":"sun","year":"2014","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276464"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.405"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1137\/080724265"},{"key":"ref5","first-page":"1033","article-title":"Fast image deconvolution using hyper-Laplacian priors","author":"krishnan","year":"2009","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.349"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2596743"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.142"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"ref45","year":"0","journal-title":"PyTorch"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206815"},{"key":"ref47","first-page":"221","article-title":"A neural approach to blind motion deblurring","author":"chakrabarti","year":"2016","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.351"},{"key":"ref41","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref44","first-page":"1","article-title":"Edge-based blur kernel estimation using patch priors","author":"sun","year":"2013","journal-title":"Proc IEEE Int Conf Comput Photogr (ICCP)"},{"key":"ref43","first-page":"1","article-title":"A method for stochastic optimization","author":"kinga","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9273274\/09000801.pdf?arnumber=9000801","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T17:19:38Z","timestamp":1651079978000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9000801\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12]]},"references-count":57,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2020.2968289","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12]]}}}