Noise2Atom: unsupervised denoising for scanning transmission electron microscopy images

We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain [Formula: see text] to a target domain [Formula: see text] , where [Formula: see text] is for our noisy experimental dataset, and...

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Detalles Bibliográficos
Autores principales: Wang, Feng, Henninen, Trond R., Keller, Debora, Erni, Rolf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Singapore 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818366/
https://www.ncbi.nlm.nih.gov/pubmed/33580362
http://dx.doi.org/10.1186/s42649-020-00041-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818366/
https://www.ncbi.nlm.nih.gov/pubmed/33580362
http://dx.doi.org/10.1186/s42649-020-00041-8

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