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Open Access Green as soon as Postprint is submitted to ZB.
3D high resolution generative deep-learning network for fluorescence microscopy imaging.
Opt. Lett. 45, 1695-1698 (2020)
Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, constrained by the physical principles of optical imaging, volume imaging has to balance imaging resolution and speed. Here, we develop a new, to the best of our knowledge, 3D deep learning network based on a dual generative adversarial network (dual-GAN) framework for recovering high-resolution (HR) volume images from high speed acquired low-resolution (LR) volume images. The proposed method does not require a precise image registration process and meanwhile guarantees the predicted HR volume image faithful to its corresponding LR volume image. The results demonstrated that our method can recover 20 x /1.0-NAvolume images from coarsely registered 5 x /0.16-NA volume images collected by light-sheet microscopy. This method. would provide great potential in applications which require high resolution volume imaging.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Mice
ISSN (print) / ISBN
0146-9592
e-ISSN
1539-4794
Journal
Optics Letters
Quellenangaben
Volume: 45,
Issue: 7,
Pages: 1695-1698
Publisher
Optical Society of America (OSA)
Publishing Place
2010 Massachusetts Ave Nw, Washington, Dc 20036 Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)