Abstract

We demonstrate single-image super-resolution in fluorescence microscopy using a trained neural-network, matching the resolution of structured illumination microscopy (SIM). This performance is achieved without any parameter estimation or assumptions about the imaging system.

© 2020 The Author(s)

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