Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network
| Autor: | 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 |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Intelligent Computing, Vol 3 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2771-5892 71127054 |
| DOI: | 10.34133/icomputing.0082 |
| Popis: | 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 propose a deep-physics-informed sparsity framework designed holistically to synergize the strengths of physical imaging models (image blurring processes), prior knowledge (continuity and sparsity constraints), a back-end optimization algorithm (image deblurring), and deep learning (an unsupervised neural network). Owing to the utilization of a multipronged learning strategy, the trained network can be applied to a variety of imaging modalities and samples to enhance the physical resolution by a factor of at least 1.67 without requiring additional training or parameter tuning. Given the advantages of high accessibility and universality, the proposed deep-physics-informed sparsity method will considerably enhance existing optical and computational imaging techniques and have a wide range of applications in biomedical research. |
| Databáze: | Directory of Open Access Journals |
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