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A 3D high resolution generative deep-learning network for fluorescence microscopy image.
bioRxiv (2019)
Deep learning technology enables us acquire high resolution image from
low resolution image in biological imaging free from sophisticated
optical hardware. However, current methods require a huge number of the
precisely registered low-resolution (LR) and high-resolution (HR) volume
image pairs. This requirement is challengeable for biological volume
imaging. Here, we proposed 3D deep learning network based on dual
generative adversarial network (dual-GAN) framework for recovering HR
volume images from LR volume images. Our network avoids learning the
direct mappings from the LR and HR volume image pairs, which need
precisely image registration process. And the cycle consistent network
makes the predicted HR volume image faithful to its corresponding LR
volume image. The proposed method achieves the recovery of 20x/1.0 NA
volume images from 5x/0.16 NA volume images collected by light-sheet
microscopy. In essence our method is suitable for the other imaging
modalities.
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Publication type
Article: Journal article
Document type
Scientific Article
Journal
bioRxiv
Publisher
Cold Spring Harbor Laboratory Press
Publishing Place
Cold Spring Harbor
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)