Deep learning for single-shot autofocus microscopy

Henry Pinkard, Zachary Phillips, Arman Babakhani, Daniel A. Fletcher, Laura Waller

Optica
June 5, 2019

Abstract. Maintaining an in-focus image over long time scales is an essential and nontrivial task for a variety of microscopy applications. Here, we describe a fast, robust autofocusing method compatible with a wide range of existing microscopes. It requires only the addition of one or a few off-axis illumi- nation sources (e.g., LEDs), and can predict the focus correc- tion from a single image with this illumination. We designed a neural network architecture, the fully connected Fourier neu- ral network (FCFNN), that exploits an understanding of the physics of the illumination to make accurate predictions with 2–3 orders of magnitude fewer learned parameters and less memory usage than existing state-of-the-art architectures, allowing it to be trained without any specialized hardware. We provide an open-source implementation of our method, to enable fast, inexpensive autofocus compatible with a variety of microscopes.

Preprint: Single-shot autofocus microscopy using deep learning
March 23, 2019  |  bioRxiv.org
Henry Pinkard, Zachary Phillips, Arman Babakhani, Daniel A. Fletcher, Laura Waller



Featured Fellows

Henry Pinkard

Computational Biology
Alumni - DATA SCIENCE FELLOW

Laura Waller

Electrical Engineering and Computer Sciences, UC Berkeley