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This paper demonstrates improved signal-to-background ratio and resolution in microscopy, suggesting deep-learning methods may help combat noise overfitting.
Fast, sensitive detector arrays make Image Scanning Microscopy (ISM) a natural successor to confocal laser scanning microscopy (CLSM), enabling super-resolution (SR) with excellent signal-to-noise ratio (SNR). While large detectors optimize photon collection, they also capture more out-of-focus light. However, the ISM dataset contains information on the axial position of the emitters. We exploit such information to directly invert the corresponding physical model with s 2 IS M , improving the signal-to-background ratio (SBR) and resolution. However, these algorithms are susceptible to noise overfitting (NO) during the iterative process. We regularize our s 2 IS M algorithm using built-in deep-learning denoisers, enabling the analysis of data with SNR previously considered unusable.
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Garrè et al. (2025) studied this question.
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