We present an ultrafast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking dataset. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.

中文翻译：

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Deep-STORM：通过深度学习的超分辨率单分子显微镜

我们提出了一种超快速，精确，无参数的方法，我们称之为Deep-STORM，用于从随机闪烁的发射器（例如用于定位显微镜的荧光分子）获得超分辨率图像。Deep-STORM使用深层卷积神经网络，可以在模拟数据或实验测量值上对其进行训练，这两种方法都得到了证明。该方法在具有挑战性的信噪比条件下和高发射器密度的情况下实现了最先进的分辨率，并且比现有方法要快得多。另外，不需要有关基础结构形状的先验信息，从而使该方法适用于任何闪烁的数据集。我们通过对模拟和实验获得的数据进行超分辨率图像重建来验证我们的方法。