2026-07-21T08:01:26Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/165082024-11-28T12:34:30Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_25179
Repisalud
author
Mandracchia, Biagio
author
Liu, Wenhao
author
Hua, Xuanwen
author
Forghani, Parvin
author
Lee, Soojung
author
Hou, Jessica
author
Nie, Shuyi
author
Xu, Chunhui
author
Jia, Shu
funder
National Institutes of Health (Estados Unidos)
funder
National Science Foundation (Estados Unidos)
funder
National Center for Advancing Translational Sciences (Estados Unidos)
funder
Office of Emerging Frontiers and Multidisciplinary Activities (Estados Unidos)
funder
Emory University (Estados Unidos)
2023-09-26T11:35:04Z
2023-09-26T11:35:04Z
2023-09
Sci Adv. 2023 Sep;9(35):eadg9245.
10.1126/sciadv.adg9245
2375-2548
Science advances
37647399
http://hdl.handle.net/20.500.12105/16508
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.
eng
Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images
research article
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