Reconstruction and regularization approaches for photon-resolved image scanning microscopy

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 s2IS 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 s2IS M algorithm using built-in deep-learning denoisers, enabling the analysis of data with SNR previously considered unusable.