Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network

Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network

As a supplement to optical super-resolution microscopy techniques, computational super-resolution methods have demonstrated remarkable results in alleviating the spatiotemporal imaging trade-off. However, they commonly suffer from low structural fidelity and universality. Therefore, we herein propos...

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Bibliographic Details
Main Authors: Zitong Ye, Yuran Huang, Jinfeng Zhang, Yunbo Chen, Hanchu Ye, Cheng Ji, Luhong Jin, Yanhong Gan, Yile Sun, Wenli Tao, Yubing Han, Xu Liu, Youhua Chen, Cuifang Kuang, Wenjie Liu
Format: Article
Language:English
Published: American Association for the Advancement of Science (AAAS) 2024-01-01
Series:Intelligent Computing
Online Access:https://spj.science.org/doi/10.34133/icomputing.0082

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https://spj.science.org/doi/10.34133/icomputing.0082

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