Dynamic Structured Illumination Microscopy with a Neural Space-time Model

TL;DR

This paper introduces Speckle Flow SIM, a neural space-time model-based method that enables super-resolution imaging of dynamic scenes using static illumination and sample motion, overcoming traditional SIM speed limitations.

Contribution

It presents a novel neural space-time modeling approach for dynamic SIM, jointly recovering motion and super-resolved images from raw data with static illumination patterns.

Findings

Successfully reconstructs dynamic scenes with deformable motion.

Achieves 1.88x resolution enhancement over diffraction limit.

Validated through simulation and inexpensive experimental setup.

Abstract

Structured illumination microscopy (SIM) reconstructs a super-resolved image from multiple raw images captured with different illumination patterns; hence, acquisition speed is limited, making it unsuitable for dynamic scenes. We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution. Speckle Flow SIM relies on sample motion to capture a sequence of raw images. 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 validate Speckle Flow SIM for coherent imaging in simulation and build a simple, inexpensive experimental setup with off-the-shelf components. We demonstrate that Speckle Flow SIM can reconstruct a dynamic scene with deformable motion and 1.88x the diffraction-limited resolution in experiment.