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Dynamic Structured Illumination Microscopy with a Neural Space-time Model

2022-06-03 05:24:06
Ruiming Cao, Fanglin Linda Liu, Li-Hao Yeh, Laura Waller
arXiv_CV
         
Abstract

Structured illumination microscopy (SIM) reconstructs a super-resolved image from multiple raw images; hence, acquisition speed is limited, making it unsuitable for dynamic scenes. We propose a new method, Speckle Flow SIM, that models sample motion during the data capture in order to reconstruct dynamic scenes with super-resolution. Speckle Flow SIM uses fixed speckle illumination and relies on sample motion to capture a sequence of raw images. Then, the spatio-temporal relationship of the dynamic scene is modeled using a neural space-time model with coordinate-based multi-layer perceptrons (MLPs), and the motion dynamics and the super-resolved scene are jointly recovered. We validated Speckle Flow SIM in simulation and built a simple, inexpensive experimental setup with off-the-shelf components. We demonstrated that Speckle Flow SIM can reconstruct a dynamic scene with deformable motion and 1.88x the diffraction-limited resolution in experiment.

Abstract (translated)

URL

https://arxiv.org/abs/2206.01397

PDF

https://arxiv.org/pdf/2206.01397.pdf

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