Surmounting photon limits and motion artifacts for biological dynamics imaging via dual-perspective self-supervised learning
Abstract Visualizing rapid biological dynamics like neuronal signaling and microvascular flow is crucial yet challenging due to photon noise and motion artifacts. Here we present a deep learning framework for enhancing the spatiotemporal relations of optical microscopy data. Our approach leverages c...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2024-01-01
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| Series: | PhotoniX |
| Online Access: | https://doi.org/10.1186/s43074-023-00117-0 |