{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:49:54Z","timestamp":1780919394148,"version":"3.54.1"},"reference-count":102,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"NSF","award":["CCF-1813910"],"award-info":[{"award-number":["CCF-1813910"]}]},{"name":"NSF","award":["CCF-2043134"],"award-info":[{"award-number":["CCF-2043134"]}]},{"DOI":"10.13039\/100007000","name":"Laboratory Directed Research and Development","doi-asserted-by":"publisher","award":["20200061DR"],"award-info":[{"award-number":["20200061DR"]}],"id":[{"id":"10.13039\/100007000","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Comput. Imaging"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tci.2021.3125564","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T21:50:21Z","timestamp":1636408221000},"page":"1400-1412","source":"Crossref","is-referenced-by-count":106,"title":["CoIL: Coordinate-Based Internal Learning for Tomographic Imaging"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7225-9677","authenticated-orcid":false,"given":"Yu","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1042-4443","authenticated-orcid":false,"given":"Jiaming","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4767-1843","authenticated-orcid":false,"given":"Brendt","family":"Wohlberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6770-3278","authenticated-orcid":false,"given":"Ulugbek","family":"Kamilov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"NeRF : Analyzing and improving neural radiance fields","author":"zhang","year":"2020"},{"key":"ref38","first-page":"7210","article-title":"NeRF in the wild: Neural radiance fields for unconstrained photo collections","author":"martin-brualla","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.28378"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3012955"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3003170"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2865356"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2021.3085534"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2020.3025735"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3004094"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2820382"},{"key":"ref27","first-page":"10","article-title":"Deep ADMM-Net for compressive sensing MRI","author":"yang","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2799231"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2629286"},{"key":"ref22","first-page":"5546","article-title":"Plug-and-play methods provably converge with properly trained denoisers","volume":"97","author":"ryu","year":"2019","journal-title":"Proc 36th Int Conf Mach Lear (ICML)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1137\/17M1122451"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2020.3006390"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2019.2893568"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa9581"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00196"},{"key":"ref100","author":"kak","year":"1988","journal-title":"Principles of Computerized Tomographic Imaging"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2020.2996385"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2839891"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2020.2977214"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2869727"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2875569"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.198"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2949470"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00177"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683057"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.300"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2599778"},{"key":"ref40","first-page":"2020","article-title":"Fourier features let networks learn high frequency functions in low dimensional domains","author":"tancik","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2176954"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.881969"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2760358"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2739299"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00273-z"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1515\/9783110524116"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2991563"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"ref46","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"0","journal-title":"Proc Med Image Comput Comput - Assist Interv (MICCAI)"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.24033\/bsmf.1625"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1038\/nature25988"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2833499"},{"key":"ref42","first-page":"41","article-title":"Sur l&#x2019;approximation, par &#x00E9;l&#x00E9;ments finis d&#x2019;ordre un, et la r&#x00E9;solution, par p&#x00E9;nalisation-dualit&#x00E9; d&#x2019;une classe de probl&#x00E8;mes de dirichlet non lin&#x00E9;aires","volume":"9","author":"glowinski","year":"1975","journal-title":"ESAIM Math Modelling Numer Anal -Mod&#x00E9;lisation Math&#x00E9;matique et Analyse Num&#x00E9;rique"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1137\/080716542"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1561\/2200000016"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/0898-1221(76)90003-1"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00664"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2018.2867611"},{"key":"ref71","first-page":"617","article-title":"View-interpolation of sparsely sampled Sinogram using convolutional neural network","volume":"10133","author":"lee","year":"2017","journal-title":"Medical Imaging 2017 Image Processing"},{"key":"ref70","article-title":"Model-Based Deep Learning","author":"shlezinger","year":"2020"},{"key":"ref76","article-title":"Data and image domain deep learning for computational imaging","author":"ghani","year":"2021"},{"key":"ref77","first-page":"631","article-title":"Metal-artifact reduction using deep-learning based Sinogram completion: Initial results","author":"claus","year":"0","journal-title":"Proc 14th Int Meeting Fully Three-Dimensional Image Reconstruction Radiol Nucl Med"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2019.2937221"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2021.3062986"},{"key":"ref78","first-page":"790","article-title":"A two-dimensional feasibility study of deep learning-based feature detection and characterization directly from CT sinograms","volume":"46","author":"man","year":"2019","journal-title":"Med Phys"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2927101"},{"key":"ref60","article-title":"Async-RED: A provably convergent asynchronous block parallel stochastic method using deep denoising priors","author":"sun","year":"0","journal-title":"Proc Int Conf Learn Representations (ICLR)"},{"key":"ref62","first-page":"3501","article-title":"Robust phase retrieval with a flexible deep network","author":"metzler","year":"0","journal-title":"Proc 35th Int Conf Mach Learn (ICML)"},{"key":"ref61","article-title":"DeepRED: Deep image prior powered by RED","author":"mataev","year":"0","journal-title":"Proc IEEE Int Conf Comput Vis Workshops (ICCVW)"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2018.2880326"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1137\/20M1337168"},{"key":"ref65","first-page":"399","article-title":"Learning fast approximation of sparse coding","author":"gregor","year":"0","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27706"},{"key":"ref67","first-page":"537","article-title":"Compressed sensing using generative models","volume":"70","author":"bora","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn (ICML)"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462233"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/83.941856"},{"key":"ref69","first-page":"2020","article-title":"Robust compressed sensing using generative models","author":"jalal","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(92)90242-F"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1364\/OE.26.002749"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1364\/OPTICA.2.000517"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1364\/OE.17.000266"},{"key":"ref91","article-title":"Nerfies: Deformable neural radiance fields","author":"park","year":"0","journal-title":"Proc IEEE Int Conf Comp Vis (ICCV)"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00741"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2021.3094062"},{"key":"ref98","article-title":"ADAM: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Representations (ICLR)"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1118\/1.4957556"},{"key":"ref96","first-page":"5301","article-title":"On the spectral bias of neural networks","author":"rahaman","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref97","first-page":"8571","article-title":"Neural tangent Kernel: Convergence and generalization in neural networks","volume":"31","author":"jacot","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12344"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1364\/OE.26.014678"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823768"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2013.6737048"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1137\/16M1102884"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2710233"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2017.2763583"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58571-6_4"},{"key":"ref17","first-page":"763","article-title":"Deep mean-shift priors for image restoration","volume":"30","author":"bigdeli","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00329"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1109\/JSTSP.2020.2999820","article-title":"SIMBA: Scalable inversion in optical tomography using deep denoising priors","volume":"14","author":"wu","year":"2020","journal-title":"IEEE J Sel Top Signal Process"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682856"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2998402"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01303-4"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27420"},{"key":"ref89","first-page":"1121","article-title":"Scene representation networks: Continuous 3D-structure-aware neural scene representations","volume":"33","author":"sitzmann","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01128"},{"key":"ref86","first-page":"2020","article-title":"Implicit neural representations with periodic activation functions","volume":"34","author":"sitzmann","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00609"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00025"}],"container-title":["IEEE Transactions on Computational Imaging"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6745852\/9318600\/9606601-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6745852\/9318600\/09606601.pdf?arnumber=9606601","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:54:09Z","timestamp":1652194449000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9606601\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":102,"URL":"https:\/\/doi.org\/10.1109\/tci.2021.3125564","relation":{},"ISSN":["2333-9403","2334-0118","2573-0436"],"issn-type":[{"value":"2333-9403","type":"electronic"},{"value":"2334-0118","type":"electronic"},{"value":"2573-0436","type":"print"}],"subject":[],"published":{"date-parts":[[2021]]}}}