AI Article Synopsis

  • Point-scanning imaging systems are popular for high-resolution imaging but struggle to optimize multiple factors like resolution and speed simultaneously.
  • The research introduces a deep learning technique called point-scanning super-resolution (PSSR) that enhances images taken with these systems by training on degraded versions of high-quality images.
  • A multi-frame approach is used to improve image quality by leveraging information from adjacent frames, making it possible to achieve better resolution, speed, and sensitivity for fluorescence time-lapse data.

Article Abstract

Point-scanning imaging systems are among the most widely used tools for high-resolution cellular and tissue imaging, benefiting 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 time-lapse data, we developed a 'multi-frame' PSSR approach that uses information in adjacent frames to improve model predictions. 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8035334PMC
http://dx.doi.org/10.1038/s41592-021-01080-zDOI Listing

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