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.

中文翻译：

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一种用于在线自动优化超分辨率光学显微镜的机器学习方法。

查找复杂成像系统（例如超分辨率显微镜）性能良好的参数化的传统方法依赖于成像任务之前在照明和采集设置上的广泛探索阶段。该策略存在几个问题：需要评估大量参数配置，导致勘探阶段表现良好的参数与成像任务之间存在差异，并且由于进行了优化和优化，导致浪费时间和资源最终成像任务是分开进行的。在这里，我们展示了一个全自动的，基于机器学习的系统，可以在成像任务的同时，朝着多个目标之间的取舍进行成像参数优化。它的潜力在各种成像任务上得到了强调，例如活细胞和多色成像以及多峰优化。该在线优化例程可以集成到各种成像系统中，以增加可访问性，优化性能并提高总体成像质量。