Learning to autofocus based on Gradient Boosting Machine for optical microscopy

Autofocus is an essential part of modern high-throughput optical microscopic imaging system, and the traditional passive autofocus algorithms such as hill climbing search are heuristics and therefore are slow and less accuracy. Instead of using the heuristics, in this paper we treat the autofocus as a regression problem and propose to learn a Gradient Boosting Machine (GBM) to predict the direction and step size simultaneously. We first leverage Fourier optical theory to explore the feasibility of predicting the step size and direction simultaneously with only one regressor. And then, inspired by Depth from Defocus (DFD), we design novel basic and combined features for faster and better autofocus. Finally, we comprehensively evaluate our methods on a dataset consisting of 2000 annotated images corresponding to 20 benchmarks of cell images. Our Defocus of Focus (DFF)-based autofocus shows improved accuracy over previous work from 97.1% to 99.9%, and significantly reduces the number of steps, achieving a 51.48% relative improvement. Code and dataset will be made publicly available.