  |
|
       |
  |
|
|
 |
 |
 |
  |
 |
 |
 |
Publications |  |
|
|
|
A machine learning approach for automated optimization of super-resolution optical microscopyAbstract - Traditional approaches for finding well-performing parameterizations of complex imaging systems, such as super-resolution microscopes rely on an extensive exploration phase over the illumination and acquisition settings, prior to the imaging task. This strategy suffers from several issues: it requires a large amount of parameter configurations to be evaluated, it leads to discrepancies between well-performing parameters in the exploration phase and imaging task, and it results in a waste of time and resources given that optimization and final imaging tasks are conducted separately. Here we show that a fully automated, machine learning-based system can conduct imaging parameter optimization toward a trade-off between several objectives, simultaneously to the imaging task. Its potential is highlighted on various imaging tasks, such as live-cell and multicolor imaging and multimodal optimization. This online optimization routine can be integrated to various imaging systems to increase accessibility, optimize performance and improve overall imaging quality.   Bibtex:
@article{Durand1204, Dernière modification: 2018/10/13 par cgagne |
|
|
|
 |
|||
|
©2002-. Laboratoire de Vision et Systèmes Numériques. Tous droits réservés |
