  We demonstrate a deep learning-based offline autofocusing method, termed
Deep-R, that is trained to rapidly and blindly autofocus a single-shot
microscopy image of a specimen that is acquired at an arbitrary out-of-focus
plane. We illustrate the efficacy of Deep-R using various tissue sections that
were imaged using fluorescence and brightfield microscopy modalities and
demonstrate snapshot autofocusing under different scenarios, such as a uniform
axial defocus as well as a sample tilt within the field-of-view. Our results
reveal that Deep-R is significantly faster when compared with standard online
algorithmic autofocusing methods. This deep learning-based blind autofocusing
framework opens up new opportunities for rapid microscopic imaging of large
sample areas, also reducing the photon dose on the sample.
