  Structured illumination microscopy (SIM) is an optical super-resolution
technique that enables live-cell imaging beyond the diffraction limit.
Reconstruction of SIM data is prone to artefacts, which becomes problematic
when imaging highly dynamic samples because previous methods rely on the
assumption that samples are static. We propose a new transformer-based
reconstruction method, VSR-SIM, that uses shifted 3-dimensional window
multi-head attention in addition to channel attention mechanism to tackle the
problem of video super-resolution (VSR) in SIM. The attention mechanisms are
found to capture motion in sequences without the need for common motion
estimation techniques such as optical flow. We take an approach to training the
network that relies solely on simulated data using videos of natural scenery
with a model for SIM image formation. We demonstrate a use case enabled by
VSR-SIM referred to as rolling SIM imaging, which increases temporal resolution
in SIM by a factor of 9. Our method can be applied to any SIM setup enabling
precise recordings of dynamic processes in biomedical research with high
temporal resolution.
