Untrained, physics-informed neural networks for structured illumination microscopy
This provides a flexible, general, and open-source method for SIM imaging that can be adapted to different illumination patterns without the need for supervised training data.
The paper tackles the problem of super-resolution image reconstruction in structured illumination microscopy (SIM) without requiring large training datasets by using a physics-informed neural network (PINN) that combines a deep neural network with the forward model of SIM, achieving resolution improvements matching theoretical expectations.
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.