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Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams
Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9626625/ https://www.ncbi.nlm.nih.gov/pubmed/36319636 http://dx.doi.org/10.1038/s41377-022-00975-6 |