دورية أكاديمية
Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network
| العنوان: | Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network |
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| المؤلفون: | 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 |
| المصدر: | Intelligent Computing, Vol 3 (2024) |
| بيانات النشر: | American Association for the Advancement of Science (AAAS), 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2771-5892 71127054 |
| العلاقة: | https://doaj.org/toc/2771-5892Test |
| DOI: | 10.34133/icomputing.0082 |
| الوصول الحر: | https://doaj.org/article/d4c968266cdf43958ad4c71127054beaTest |
| رقم الانضمام: | edsdoj.4c968266cdf43958ad4c71127054bea |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 27715892 71127054 |
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| DOI: | 10.34133/icomputing.0082 |