论文标题
未经训练的物理信息的神经网络，用于结构化照明显微镜
Untrained, physics-informed neural networks for structured illumination microscopy
论文作者
论文摘要
近年来，人们对使用深层神经网络（DNN）进行超分辨率图像重建，包括结构化照明显微镜（SIM）。尽管这些方法显示出非常有希望的结果，但它们都依赖于数据驱动的，有监督的培训策略，这些培训策略需要大量的地面真相图像，这在实验上很难实现。对于SIM成像，存在需要一种灵活，一般和开源的重建方法，该方法可以很容易地适应不同形式的结构化照明。我们证明，我们可以将深层神经网络与结构化照明过程的正向模型相结合，以在没有训练数据的情况下重建子分量图像。可以在一组衍射有限的子图像上优化产生的物理信息神经网络（PINN），因此不需要任何训练集。我们通过模拟和实验数据显示，可以通过简单地更改损失函数中使用的已知照明模式，并可以实现与理论期望非常匹配的分辨率改进，将此Pinn应用于多种SIM方法。
In recent years there has been great interest in using deep neural networks (DNN) for super-resolution image reconstruction including for structured illumination microscopy (SIM). While these methods have shown very promising results, they all rely on data-driven, supervised training strategies that need a large number of ground truth images, which is experimentally difficult to realize. For SIM imaging, there exists a need for a flexible, general, and open-source reconstruction method that can be readily adapted to different forms of structured illumination. We demonstrate that we can combine a deep neural network with the forward model of the structured illumination process to reconstruct sub-diffraction images without training data. The resulting physics-informed neural network (PINN) can be optimized on a single set of diffraction limited sub-images and thus doesn't require any training set. We show with simulated and experimental data that this PINN can be applied to a wide variety of SIM methods by simply changing the known illumination patterns used in the loss function and can achieve resolution improvements that match well with theoretical expectations.
