Dynamic Structured Illumination Microscopy with a Neural Space-time Model
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866909648667279360 |
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| author | Cao, Ruiming Liu, Fanglin Linda Yeh, Li-Hao Waller, Laura |
| author_facet | Cao, Ruiming Liu, Fanglin Linda Yeh, Li-Hao Waller, Laura |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_01397 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Dynamic Structured Illumination Microscopy with a Neural Space-time Model Cao, Ruiming Liu, Fanglin Linda Yeh, Li-Hao Waller, Laura Optics Computer Vision and Pattern Recognition Graphics Image and Video Processing Signal Processing 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. |
| title | Dynamic Structured Illumination Microscopy with a Neural Space-time Model |
| topic | Optics Computer Vision and Pattern Recognition Graphics Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2206.01397 |