The visualization and analysis of biological events using fluorescence microscopy is limited by the noise inherent in the images obtained. Now, a self-supervised spatial redundancy denoising transformer is proposed to address this challenge.
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References
Li, X. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00568-2 (2023).
Weigert, M. et al. Nat. Meth. 15, 1090–1097 (2018).
Lehtinen, J. et al. In Proc. 35th Int. Conf. on Machine Learning 2965–2974 (PMLR, 2018).
Li, X. et al. Nat. Meth. 18, 1395–1400 (2021).
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Whitehead, L. Moving towards a generalized denoising network for microscopy. Nat Comput Sci 3, 1013–1014 (2023). https://doi.org/10.1038/s43588-023-00574-4
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DOI: https://doi.org/10.1038/s43588-023-00574-4
