Untrained, physics-informed neural networks for structured illumination microscopy

• URL: http://arxiv.org/abs/2207.07705v1
• Date: Fri, 15 Jul 2022 19:02:07 GMT
• Title: Untrained, physics-informed neural networks for structured illumination microscopy
• Authors: Zachary Burns, Zhaowei Liu
• Abstract summary: We show 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.
• Score: 0.456877715768796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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