# Single-shot autofocusing of microscopy images using deep learning

arXiv: https://arxiv.org/abs/2003.09585 · HTML: https://mlsanity.com/paper/2003.09585/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2020.

## Built on by (most cited first, 11 of 11)

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- [Neural Network-Based Processing and Reconstruction of Compromised Biophotonic Image Data](https://mlsanity.com/paper/2403.14324/) (2024)
- [Semi-supervised machine learning model for analysis of nanowire morphologies from transmission electron microscopy images](https://mlsanity.com/paper/2203.13875/) (2022)
- [Ultrafast Focus Detection for Automated Microscopy](https://mlsanity.com/paper/2108.12050/) (2021)
- [Preparing data for pathological artificial intelligence with clinical-grade performance](https://mlsanity.com/paper/2205.10748/) (2022)
- [Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space](https://mlsanity.com/paper/2307.15461/) (2023)
- [Physics-informed generative real-time lens-free imaging](https://mlsanity.com/paper/2403.07786/) (2024)

## Abstract (arXiv)

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.