Dynamic Structured Illumination Microscopy with a Neural Space-time Model
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 47 references that are in the map. First posted on arXiv 3 Jun 2022.
Builds on
12 of 47 references.
References · 12
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- 2016Structured illumination microscopy with unknown patterns and a statistical priorcited by 2
- 2018Computational structured illumination for high-content fluorescent and phase microscopycited by 1
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.