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dc.contributor.author | Mandracchia, Biagio | |
dc.contributor.author | Liu, Wenhao | |
dc.contributor.author | Hua, Xuanwen | |
dc.contributor.author | Forghani, Parvin | |
dc.contributor.author | Lee, Soojung | |
dc.contributor.author | Hou, Jessica | |
dc.contributor.author | Nie, Shuyi | |
dc.contributor.author | Xu, Chunhui | |
dc.contributor.author | Jia, Shu | |
dc.date.accessioned | 2023-09-26T11:35:04Z | |
dc.date.available | 2023-09-26T11:35:04Z | |
dc.date.issued | 2023-09 | |
dc.identifier.citation | Sci Adv. 2023 Sep;9(35):eadg9245. | es_ES |
dc.identifier.uri | http://hdl.handle.net/20.500.12105/16508 | |
dc.description | Incluye: artículo, material suplementario, videos y software. | es_ES |
dc.description.abstract | Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy. | es_ES |
dc.description.sponsorship | We acknowledge the support of the National Institutes of Health grants R35GM124846 (to S.J.) and R01AA028527 (to C.X.), the National Science Foundation grants BIO2145235 and EFMA1830941 (to S.J.), and Marvin H. and Nita S. Floyd Research Fund (to S.J.). This research project was supported, in part, by the Emory University Integrated Cellular Imaging Microscopy Core and by PHS Grant UL1TR000454 from the Clinical and Translational Science Award Program, National Institutes of Health, and National Center for Advancing Translational Sciences. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | American Association for the Advancement of Science (AAAS) | es_ES |
dc.type.hasVersion | VoR | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
dc.subject.mesh | Algorithms | es_ES |
dc.subject.mesh | Electrons | es_ES |
dc.subject.mesh | Microscopy, Fluorescence | es_ES |
dc.subject.mesh | Image Processing, Computer-Assisted | es_ES |
dc.subject.mesh | Neural Networks, Computer | es_ES |
dc.title | Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images | es_ES |
dc.type | research article | es_ES |
dc.rights.license | Atribución-NoComercial 4.0 Internacional | * |
dc.identifier.pubmedID | 37647399 | es_ES |
dc.format.volume | 9 | es_ES |
dc.format.number | 35 | es_ES |
dc.format.page | eadg9245 | es_ES |
dc.identifier.doi | 10.1126/sciadv.adg9245 | es_ES |
dc.contributor.funder | National Institutes of Health (Estados Unidos) | es_ES |
dc.contributor.funder | National Science Foundation (Estados Unidos) | es_ES |
dc.contributor.funder | Office of Emerging Frontiers and Multidisciplinary Activities (Estados Unidos) | es_ES |
dc.contributor.funder | Emory University (Estados Unidos) | es_ES |
dc.contributor.funder | National Center for Advancing Translational Sciences (Estados Unidos) | es_ES |
dc.description.peerreviewed | Sí | es_ES |
dc.identifier.e-issn | 2375-2548 | es_ES |
dc.relation.publisherversion | https://doi.org/10.1126/sciadv.adg9245 | es_ES |
dc.identifier.journal | Science advances | es_ES |
dc.repisalud.centro | ISCIII::Centro Nacional de Microbiología::Unidades Comunes Científico-Técnicas (UCCT) | es_ES |
dc.repisalud.institucion | ISCIII | es_ES |
dc.rights.accessRights | open access | es_ES |