{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T22:26:44Z","timestamp":1771626404035,"version":"3.50.1"},"reference-count":87,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2024YFC3406400"],"award-info":[{"award-number":["2024YFC3406400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371007"],"award-info":[{"award-number":["62371007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Biomedical Computing Platform of National Biomedical Imaging Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tip.2026.3651963","type":"journal-article","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T21:01:14Z","timestamp":1768338074000},"page":"1582-1594","source":"Crossref","is-referenced-by-count":0,"title":["Blind Inversion Using Latent Diffusion Priors"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2888-0771","authenticated-orcid":false,"given":"Weimin","family":"Bai","sequence":"first","affiliation":[{"name":"Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China"}]},{"given":"Siyi","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Physics, Peking University, Beijing, China"}]},{"given":"Wenzheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Wangxuan Institute of Computer Technology, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1526-6787","authenticated-orcid":false,"given":"He","family":"Sun","sequence":"additional","affiliation":[{"name":"National Biomedical Imaging Center, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003032755"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/344779.344972"},{"key":"ref3","article-title":"Inverse rendering for computer graphics","author":"Marschner","year":"1998"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1364\/AOP.10.000409"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-020-0174-8"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.142"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/ab6d57"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2739299"},{"key":"ref10","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref11","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2020","journal-title":"arXiv:2011.13456"},{"key":"ref12","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein"},{"key":"ref13","first-page":"11918","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Song"},{"key":"ref14","article-title":"Diffusion posterior sampling for general noisy inverse problems","author":"Chung","year":"2022","journal-title":"arXiv:2209.14687"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref16","first-page":"71340","article-title":"AUDIT: Audio editing by following instructions with latent diffusion models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref17","article-title":"LDM3D: Latent diffusion model for 3D","author":"Melech","year":"2023","journal-title":"arXiv:2305.10853"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02161"},{"key":"ref19","first-page":"49960","article-title":"Solving linear inverse problems provably via posterior sampling with latent diffusion models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rout"},{"key":"ref20","article-title":"Solving inverse problems with latent diffusion models via hard data consistency","author":"Song","year":"2023","journal-title":"arXiv:2307.08123"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00519"},{"key":"ref22","article-title":"Parallel diffusion models of operator and image for blind inverse problems","author":"Chung","year":"2022","journal-title":"arXiv:2211.10656"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00853"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3847\/1538-4357\/aab6b5"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/83.236536"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2176954"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00984"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1088\/0266-5611\/23\/3\/008"},{"key":"ref29","article-title":"Zero-shot image restoration using denoising diffusion null-space model","author":"Wang","year":"2022","journal-title":"arXiv:2212.00490"},{"key":"ref30","first-page":"25683","article-title":"Improving diffusion models for inverse problems using manifold constraints","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chung"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01209"},{"key":"ref32","article-title":"Efficient Bayesian computational imaging with a surrogate score-based prior","author":"Feng","year":"2023","journal-title":"arXiv:2309.01949"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00742"},{"key":"ref34","article-title":"Solving inverse problems in medical imaging with score-based generative models","author":"Song","year":"2021","journal-title":"arXiv:2111.08005"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102479"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10584-0_11"},{"key":"ref37","article-title":"Learning to predict 3D objects with an interpolation-based differentiable renderer","volume-title":"Proc. Adv. Neural Inf. Process. Syst","author":"Chen"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"ref39","article-title":"DreamFusion: Text-to-3D using 2D diffusion","author":"Poole","year":"2022","journal-title":"arXiv:2209.14988"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00037"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00421"},{"key":"ref42","article-title":"MVDream: Multi-view diffusion for 3D generation","author":"Shi","year":"2023","journal-title":"arXiv:2308.16512"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.5040\/9781501365171.2337"},{"key":"ref44","article-title":"ShapeNet: An information-rich 3D model repository","author":"Chang","year":"2015","journal-title":"arXiv:1512.03012"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01263"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02086"},{"key":"ref47","first-page":"8406","article-title":"ProlificDreamer: High-fidelity and diverse text-to-3D generation with variational score distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02033"},{"key":"ref49","article-title":"Diffusion with forward models: Solving stochastic inverse problems without direct supervision","author":"Tewari","year":"2023","journal-title":"arXiv:2306.11719"},{"key":"ref50","volume-title":"Zero 123++: A single image to consistent multi-view diffusion base model","author":"Shi","year":"2023"},{"key":"ref51","first-page":"22226","article-title":"One-2\u20133\u201345: Any single image to 3D mesh in 45 seconds without per-shape optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref52","article-title":"One-2\u20133\u2013$45++$\n: Fast single image to 3D objects with consistent multi-view generation and 3D diffusion","author":"Liu","year":"2023","journal-title":"arXiv:2311.07885"},{"key":"ref53","article-title":"LEAP: Liberate sparse-view 3D modeling from camera poses","author":"Jiang","year":"2023","journal-title":"arXiv:2310.01410"},{"key":"ref54","article-title":"Prompt-tuning latent diffusion models for inverse problems","author":"Chung","year":"2023","journal-title":"arXiv:2310.01110"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.2307\/2984875"},{"key":"ref56","first-page":"11592","article-title":"DeepGEM: Generalized expectation-maximization for blind inversion","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Gao"},{"key":"ref57","volume-title":"Probabilistic Machine Learning: Advanced Topics","author":"Murphy","year":"2023"},{"key":"ref58","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dhariwal"},{"key":"ref59","article-title":"Pseudoinverse-guided diffusion models for inverse problems","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Song"},{"key":"ref60","first-page":"14938","article-title":"Robust compressed sensing MRI with deep generative priors","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jalal"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2551244"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ICCPhot.2013.6528301"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/83.392335"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3088914"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2970919"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33715-4_5"},{"key":"ref69","first-page":"25501","article-title":"GibbsDDRM: A partially collapsed Gibbs sampler for solving blind inverse problems with denoising diffusion restoration","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Murata"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2753804"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01458"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00897"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00340"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.188"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01344"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72952-2_5"},{"issue":"4","key":"ref77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3592433","article-title":"3D Gaussian splatting for real-time radiance field rendering","volume":"42","author":"Kerbl","year":"2023","journal-title":"ACM Trans. Graph."},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01211"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00540"},{"key":"ref80","first-page":"26389","article-title":"SAMURAI: Shape and material from unconstrained real-world arbitrary image collections","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Boss"},{"key":"ref81","first-page":"8941","article-title":"Prompt-tuning latent diffusion models for inverse problems","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chung"},{"key":"ref82","article-title":"Solving inverse problems with FLAIR","author":"Erbach","year":"2025","journal-title":"arXiv:2506.02680"},{"key":"ref83","article-title":"Regularization by texts for latent diffusion inverse solvers","volume-title":"Proc. 13th Int. Conf. Learn. Represent.","author":"Kim"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2011.tm11181"},{"key":"ref85","article-title":"FlowDPS: Flow-driven posterior sampling for inverse problems","author":"Kim","year":"2025","journal-title":"arXiv:2503.08136"},{"key":"ref86","volume-title":"Spherical Coordinate System","year":"2008"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19797-0_42"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/83\/11355710\/11351303.pdf?arnumber=11351303","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T21:19:42Z","timestamp":1771622382000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11351303\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":87,"URL":"https:\/\/doi.org\/10.1109\/tip.2026.3651963","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}