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• W2795453812 abstract "Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but thus far have required user inputs that presume a priori knowledge about the sample. Here, we develop a flexible illumination control system for localization microscopy comprised of two interacting components that require no sample-specific inputs: a self-tuning controller and a deep learning molecule density estimator that is accurate over an extended range. This system obviates the need to fine-tune parameters and demonstrates the design of modular illumination control for localization microscopy." @default.
• W2795453812 created "2018-04-13" @default.
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• W2795453812 date "2018-03-27" @default.
• W2795453812 modified "2023-10-02" @default.
• W2795453812 title "Design Principles for Autonomous Illumination Control in Localization Microscopy - Data" @default.
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• W2795453812 doi "https://doi.org/10.5281/zenodo.1212352" @default.
• W2795453812 hasPublicationYear "2018" @default.
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