Dynamic Structured Illumination Microscopy with a Neural Space-time Model
This method addresses the limitation of SIM for imaging dynamic scenes in microscopy, offering a practical solution for researchers in biological imaging, though it is incremental as it builds on existing SIM techniques with a novel modeling approach.
The paper tackles the problem of slow acquisition speed in structured illumination microscopy (SIM) for dynamic scenes by proposing Speckle Flow SIM, which uses static patterned illumination with moving samples and a neural space-time model to jointly recover motion dynamics and super-resolved scenes, achieving 1.88x the diffraction-limited resolution in experiments.
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.