Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams
We present experimentally unsupervised learned deconvolution for light-sheet microscopy using propagation-invariant beams. By training a generative adversarial network using simulated paired images consistent with the physics of the imaging system, and real unpaired experimental data, we create a ne...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2022-11-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-022-00975-6 |