{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:21:47Z","timestamp":1784172107640,"version":"3.55.0"},"reference-count":112,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372016"],"award-info":[{"award-number":["62372016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology","award":["2024B1212010006"],"award-info":[{"award-number":["2024B1212010006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1109\/tpami.2025.3538896","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T18:51:30Z","timestamp":1738781490000},"page":"3992-4006","source":"Crossref","is-referenced-by-count":30,"title":["Invertible Diffusion Models for Compressed Sensing"],"prefix":"10.1109","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9056-3717","authenticated-orcid":false,"given":"Bin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1417-7240","authenticated-orcid":false,"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8234-5533","authenticated-orcid":false,"given":"Weiqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4993-5416","authenticated-orcid":false,"given":"Chen","family":"Zhao","sequence":"additional","affiliation":[{"name":"King Abdullah University of Science and Technology, Thuwal, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8466-8061","authenticated-orcid":false,"given":"Shijie","family":"Zhao","sequence":"additional","affiliation":[{"name":"ByteDance Inc, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9765-4523","authenticated-orcid":false,"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-3125","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Computer Engineering, Peking University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.20132"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JRPROC.1949.232969"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914730"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21391"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1118\/1.2836423"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/55\/21\/005"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3023869"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3059911"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2023.3338272"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01698"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19790-1_41"},{"key":"ref13","first-page":"37749","article-title":"Degradation-aware unfolding half-shuffle transformer for spectral compressive imaging","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Cai"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2323127"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2014.2329449"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2527181"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ALLERTON.2015.7447163"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.55"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.3016905"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3088914"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2022.3199595"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1137\/16M1102884"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01814-w"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3204461"},{"key":"ref25","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00129"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02168-7"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611866"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73202-7_25"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02405"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00177"},{"key":"ref32","first-page":"23593","article-title":"Denoising diffusion restoration models","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Kawar"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00598"},{"key":"ref34","first-page":"1","article-title":"Diffusion posterior sampling for general noisy inverse problems","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chung"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01204"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01025"},{"key":"ref39","first-page":"1","article-title":"Zero-shot image restoration using denoising diffusion null-space model","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2017.09.010"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3274988"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01257"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3132489"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58604-1_31"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2024.3504490"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2022.3208394"},{"key":"ref49","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Ho"},{"key":"ref50","first-page":"1","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song"},{"key":"ref51","first-page":"1","article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530757"},{"key":"ref53","first-page":"14715","article-title":"Diffusion models as plug-and-play priors","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Graikos"},{"key":"ref54","first-page":"25683","article-title":"Improving diffusion models for inverse problems using manifold constraints","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Chung"},{"key":"ref55","first-page":"1","article-title":"Pseudoinverse-guided diffusion models for inverse problems","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Song"},{"key":"ref56","first-page":"10236","article-title":"Glow: Generative flow with invertible 1x1 convolutions","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Kingma"},{"key":"ref57","first-page":"2722","article-title":"Flow++: Improving flow-based generative models with variational dequantization and architecture de?sign","volume-title":"Proc. Int. Conf. on Mach. Learn.","author":"Ho"},{"key":"ref58","first-page":"2211","article-title":"The reversible residual network: Backpropagation without storing activations","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Gomez"},{"key":"ref59","first-page":"11004","article-title":"MintNet: Building invertible neural networks with masked convolutions","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Song"},{"key":"ref60","first-page":"9043","article-title":"Reversible recurrent neural networks","volume-title":"Proc. Neural Inf. Process. Syst.","author":"MacKay"},{"key":"ref61","first-page":"6437","article-title":"Training graph neural networks with 1000 layers","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01056"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01316"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3184845"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02158"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00669"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aaf14a"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1038\/323533a0"},{"key":"ref69","article-title":"Prompt-tuning latent diffusion models for inverse problems","author":"Chung","year":"2023"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01581"},{"key":"ref71","first-page":"1","article-title":"BK-SDM: Architecturally compressed stable diffusion for efficient text-to-image generation","volume-title":"Proc. Int. Conf. Mach. Learn. Workshops","author":"Kim"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01499"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00196"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2928136"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.2977507"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3023629"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_31"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_14"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/ICME51207.2021.9428249"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3091834"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3044472"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475562"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01688"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00875"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP43922.2022.9746648"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3195319"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3217365"},{"key":"ref89","first-page":"49960","article-title":"Solving linear inverse problems provably via posterior sampling with latent diffusion models","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Rout"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00958"},{"key":"ref91","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Dhariwal"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref93","first-page":"25278","article-title":"LAION-5B: An open large-scale dataset for training next generation image-text models","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Schuhmann"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref95","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2631888"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3397012"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/ICDSP.2007.4288604"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2011.2170977"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1109\/MMSP.2017.8122281"},{"key":"ref101","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3088611"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02209-1"},{"key":"ref103","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Paszke"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2001.937655"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref108","first-page":"6629","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Heusel"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref110","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth817"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1364\/OE.519045"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16366"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10958761\/10874182.pdf?arnumber=10874182","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T18:19:07Z","timestamp":1744654747000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10874182\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":112,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2025.3538896","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5]]}}}