2026-07-21T08:01:23Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/165082024-11-28T12:34:30Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_25179

00925njm 22002777a 4500 dc 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 author 2023-09 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. Sci Adv. 2023 Sep;9(35):eadg9245. 10.1126/sciadv.adg9245 2375-2548 Science advances 37647399 http://hdl.handle.net/20.500.12105/16508 Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images