Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging

Abstract Detection noise significantly degrades the quality of structured illumination microscopy (SIM) images, especially under low-light conditions. Although supervised learning based denoising methods have shown prominent advances in eliminating the noise-induced artifacts, the requirement of a l...

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Xehetasun bibliografikoak

Egile Nagusiak:	Xingye Chen, Chang Qiao, Tao Jiang, Jiahao Liu, Quan Meng, Yunmin Zeng, Haoyu Chen, Hui Qiao, Dong Li, Jiamin Wu
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	SpringerOpen 2024-03-01
Saila:	PhotoniX
Gaiak:	Super-resolution microscopy Structured illumination microscopy Deep learning Self-supervised learning Live-cell imaging
Sarrera elektronikoa:	https://doi.org/10.1186/s43074-024-00121-y
Etiketak:	Etiketa erantsi Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!