{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T10:29:09Z","timestamp":1760956149931,"version":"3.37.3"},"reference-count":62,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tip.2022.3143006","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T21:49:33Z","timestamp":1643320173000},"page":"1628-1640","source":"Crossref","is-referenced-by-count":15,"title":["BIGPrior: Toward Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration"],"prefix":"10.1109","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7469-2404","authenticated-orcid":false,"given":"Majed","family":"El Helou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0441-6068","authenticated-orcid":false,"given":"Sabine","family":"Susstrunk","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.38"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.901238"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/34.56205"},{"key":"ref4","first-page":"1033","article-title":"Fast image deconvolution using hyper-Laplacian priors","volume-title":"Proc. Neural Inf. Process. Syst. (NeurIPS)","author":"Krishnan"},{"article-title":"Progressive growing of GANs for improved quality, stability, and variation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Karras","key":"ref5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2976814"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_45"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_44"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00984"},{"key":"ref12","article-title":"BM3D image denoising with shape-adaptive principal component analysis","author":"Dabov","year":"2009","journal-title":"Signal Processing with Adaptive Sparse Structured Representations"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1137\/120874989"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2329449"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459452"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299163"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2012.2226445"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/18.382009"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.888335"},{"key":"ref20","volume-title":"Pierre-Simon Laplace Philosophical Essay on Probabilities: Translated From the Fifth French Edition of 1825 With Notes by the Translator.","volume":"13","author":"Laplace","year":"1998"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(92)90242-F"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/78.923297"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.366"},{"key":"ref24","first-page":"769","article-title":"Natural image denoising with convolutional networks","volume-title":"Proc. Neural Inf. Process. Syst. (NeurIPS)","author":"Jain"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00457"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2969348"},{"article-title":"Residual non-local attention networks for image restoration","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Zhang","key":"ref29"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00485"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00352"},{"article-title":"Adversarial feature learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Donahue","key":"ref32"},{"key":"ref33","first-page":"1","article-title":"Source generator attribution via inversion","volume-title":"Proc. Comput. Vis. Pattern Recognit. (CVPR) Workshops","author":"Albright"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00333"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00308"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_16"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540214"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451147"},{"key":"ref39","article-title":"Self-supervised fast adaptation for denoising via meta-learning","volume-title":"arXiv:2001.02899","author":"Lee","year":"2020"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3323023"},{"key":"ref41","first-page":"9229","article-title":"Test-time training with self-supervision for generalization under distribution shifts","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Sun"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.145"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2362057"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00251"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00577"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9054746"},{"key":"ref47","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","volume-title":"arXiv:1506.03365","author":"Yu","year":"2015"},{"key":"ref48","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","volume-title":"arXiv:1511.06434","author":"Radford","year":"2015"},{"article-title":"LR-GAN: Layered recursive generative adversarial networks for image generation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Yang","key":"ref49"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.299"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.72"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref54","first-page":"1","article-title":"SGDR: Stochastic gradient descent with warm restarts","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Loshchilov"},{"key":"ref55","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Simonyan"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00182"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2596743"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2006.881199"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126278"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00181"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.04.045"},{"key":"ref62","first-page":"1690","article-title":"Variational denoising network: Toward blind noise modeling and removal","volume-title":"Proc. Neural Inf. Process. Syst. (NeurIPS)","volume":"32","author":"Yue"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9626658\/09694512.pdf?arnumber=9694512","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T22:28:38Z","timestamp":1705184918000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9694512\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":62,"URL":"https:\/\/doi.org\/10.1109\/tip.2022.3143006","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"type":"print","value":"1057-7149"},{"type":"electronic","value":"1941-0042"}],"subject":[],"published":{"date-parts":[[2022]]}}}