Single-shot autofocusing of microscopy images using deep learning
This enables rapid microscopic imaging of large sample areas with reduced photon dose, addressing a bottleneck in microscopy workflows.
The paper tackled the problem of autofocusing microscopy images from a single out-of-focus shot using a deep learning method called Deep-R, achieving significantly faster performance compared to standard online algorithmic methods.
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.