{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:31:52Z","timestamp":1761294712449,"version":"3.40.2"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":["2022YFA1204201"],"award-info":[{"award-number":["2022YFA1204201"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62201628","62101611"],"award-info":[{"award-number":["62201628","62101611"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515011375","2023A1515012278","2023A1515011780"],"award-info":[{"award-number":["2022A1515011375","2023A1515012278","2023A1515011780"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220530145411027","JCYJ20220818102414031"],"award-info":[{"award-number":["JCYJ20220530145411027","JCYJ20220818102414031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/tim.2024.3375967","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T18:03:58Z","timestamp":1710180238000},"page":"1-10","source":"Crossref","is-referenced-by-count":4,"title":["Self-Fusion Simplex Noise-Based Diffusion Model for Self-Supervised Low-Dose Digital Radiography Denoising"],"prefix":"10.1109","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1041-1092","authenticated-orcid":false,"given":"Yanyang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4005-4292","authenticated-orcid":false,"given":"Zirong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7447-9956","authenticated-orcid":false,"given":"Weifei","family":"Wu","sequence":"additional","affiliation":[{"name":"The First College of Clinical Medical Science, China Three Gorges University, Yichang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5048-5606","authenticated-orcid":false,"given":"Jianjia","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8295-5104","authenticated-orcid":false,"given":"Weiwen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Guangdong, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00405-x"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-023-00831-y"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_49"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3128703"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.flowmeasinst.2005.02.003"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2015.2485341"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3271714"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.1999.817091"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2011.6116449"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/19.744358"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2910338"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3078555"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1128\/JB.05227-11"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2715284"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00984"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01454"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/83.902288"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2022.3176486"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3233\/XST-17356"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"},{"key":"ref21","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. 34th Int. Conf. Neural Inf. Process. Syst.","author":"Ho"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3073174"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1117\/12.2612235"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01118"},{"article-title":"Diffusion posterior sampling for general noisy inverse problems","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Chung","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01043"},{"key":"ref28","article-title":"Diffusion models for medical image analysis: A comprehensive survey","author":"Kazerouni","year":"2022","journal-title":"arXiv:2211.07804"},{"key":"ref29","article-title":"Noise estimation for generative diffusion models","author":"San-Roman","year":"2021","journal-title":"arXiv:2104.02600"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.142.3.609"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2019.2925881"},{"key":"ref32","article-title":"Perlin noise improve adversarial robustness","author":"Tang","year":"2021","journal-title":"arXiv:2112.13408"},{"key":"ref33","first-page":"29514","article-title":"Sample complexity bounds for learning high-dimensional simplices in noisy regimes","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Saberi"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICIPTM52218.2021.9388367"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3050850"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.7.002912"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102846"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3351382"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1118\/1.598410"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2195669"},{"issue":"23","key":"ref41","first-page":"495","article-title":"Introduction to convolutional neural networks","volume":"5","author":"Wu","year":"2017","journal-title":"Nat. Key Lab Novel Softw. Technol."},{"key":"ref42","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein"},{"key":"ref43","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol"},{"article-title":"Denoising diffusion implicit models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Song","key":"ref44"},{"article-title":"Deep complex networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Trabelsi","key":"ref45"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102859"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2023.3328278"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2017.12.012"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2024.3376414"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3325824"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00080"},{"key":"ref52","first-page":"1415","article-title":"Maximum likelihood training of score-based diffusion models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Song"},{"key":"ref53","article-title":"Understanding diffusion models: A unified perspective","author":"Luo","year":"2022","journal-title":"arXiv:2208.11970"},{"key":"ref54","article-title":"Denoising diffusion implicit models","author":"Song","year":"2020","journal-title":"arXiv:2010.02502"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07942-6"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2018.01.010"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/10367905\/10466592.pdf?arnumber=10466592","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T19:10:30Z","timestamp":1742584230000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10466592\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/tim.2024.3375967","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"type":"print","value":"0018-9456"},{"type":"electronic","value":"1557-9662"}],"subject":[],"published":{"date-parts":[[2024]]}}}