Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefitting from arbitrarily defined pixel sizes. The resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point-scanning systems are difficult to optimize simultaneously. We show these limitations can be mitigated via the use of Deep Learning-based supersampling of undersampled images acquired on a point-scanning system, which we term point-scanning super-resolution (PSSR) imaging. We designed a “crappifier” that computationally degrades high SNR, high pixel resolution ground truth images to simulate low SNR, low-resolution counterparts for training PSSR models that can restore real-world undersampled images. For high spatiotemporal resolution fluorescence timelapse data, we developed a “multi-frame” PSSR approach that utilizes information in adjacent frames to improve model predictions. In conclusion, PSSR facilitates point-scanning image acquisition with otherwise unattainable resolution, speed, and sensitivity. All the training data, models, and code for PSSR are publicly available at 3DEM.org . Editor’s summary Point-scanning super-resolution imaging uses deep learning to supersample undersampled images and enable time-lapse imaging of subcellular events. An accompanying “crappifier” rapidly generates quality training data for robust performance.

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