A machine learning approach for online automated optimization of super-resolution optical microscopy
oleh: Audrey Durand, Theresa Wiesner, Marc-André Gardner, Louis-Émile Robitaille, Anthony Bilodeau, Christian Gagné, Paul De Koninck, Flavie Lavoie-Cardinal
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2018-12-01 |
Deskripsi
Complex imaging systems like super-resolution microscopes currently require laborious parameter optimization before imaging. Here, the authors present an imaging optimization framework based on machine learning that performs simultaneous parameter optimization to simplify this procedure for a wide range of imaging tasks.