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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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| Pubblicato in: | Appl Microsc |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Springer Singapore
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7818366/ https://ncbi.nlm.nih.gov/pubmed/33580362 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s42649-020-00041-8 |
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