{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T17:44:38Z","timestamp":1761759878843,"version":"build-2065373602"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62466004","61962007","62266009"],"award-info":[{"award-number":["62466004","61962007","62266009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"Key Projects of Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2018GXNSFDA294001"],"award-info":[{"award-number":["2018GXNSFDA294001"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Key Laboratory of Big Data in Finance and Economics","award":["FEDOP2022A06"],"award-info":[{"award-number":["FEDOP2022A06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3621837","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T17:40:14Z","timestamp":1760550014000},"page":"182149-182164","source":"Crossref","is-referenced-by-count":0,"title":["Robust Self-Supervised Real-Image Denoising via Consensual Contrastive Regularization as Preserving Force"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3533-462X","authenticated-orcid":false,"given":"Kaiqi","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Guangxi University of Science and Technology, Liuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2309-7282","authenticated-orcid":false,"given":"Zhiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, Guangxi University of Science and Technology, Liuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2949-7241","authenticated-orcid":false,"given":"Jiayi","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Statistics, Guangxi University of Science and Technology, Liuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rongyi","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Automation, Guangxi University of Science and Technology, Liuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Automation, Guangxi University of Science and Technology, Liuzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2005.38"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1117\/12.643267"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2014.366"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2910879"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3016766"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3415420"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00182"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20071-7_2"},{"key":"ref9","article-title":"KBNet: Kernel basis network for image restoration","author":"Zhang","year":"2023","journal-title":"arXiv:2303.02881"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52733.2024.00272"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i5.32506"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.00956"},{"key":"ref13","first-page":"1","article-title":"PUCA: Patch-unshuffle and channel attention for enhanced self-supervised image denoising","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Jang"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.01347"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00292"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52733.2024.02380"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52733.2024.00263"},{"key":"ref18","article-title":"Real-world noisy image denoising: A new benchmark","author":"Xu","year":"2018","journal-title":"arXiv:1804.02603"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.186"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2019.2909805"},{"key":"ref21","article-title":"A comparison of image denoising methods","author":"Kong","year":"2023","journal-title":"arXiv:2304.08990"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2017.2662206"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2022.3216681"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00325"},{"key":"ref25","first-page":"1","article-title":"Variational denoising network: Toward blind noise modeling and removal","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Yue"},{"key":"ref26","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv:2010.11929"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01716"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.00564"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref30","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"arXiv:1511.06434"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"issue":"1","key":"ref33","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.32604\/cmc.2025.065232","article-title":"Super-resolution generative adversarial network with pyramid attention module for face generation","volume":"85","author":"Srinivasu","year":"2025","journal-title":"Comput., Mater. Continua"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00235"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01162"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7009"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01706"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01720"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.01741"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.01116"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.00591"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.00169"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00612"},{"key":"ref44","article-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2017","journal-title":"arXiv:1706.06083"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.01128"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52733.2024.02454"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.90"},{"key":"ref48","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Radford"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.00975"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.01041"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2019.00065"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52729.2023.00560"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/iccv51070.2023.01187"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.01454"},{"key":"ref56","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11204178.pdf?arnumber=11204178","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T17:40:16Z","timestamp":1761759616000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11204178\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":56,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3621837","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2025]]}}}