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...

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Detalles Bibliográficos
Autores principales: Wijesinghe, Philip, Corsetti, Stella, Chow, Darren J. X., Sakata, Shuzo, Dunning, Kylie R., Dholakia, Kishan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
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

Internet

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

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