High-precision microscopic autofocus with a single natural image

Abstract

In industrial microscopic detection, learning-based autofocus methods have empowered operators to acquire high-quality images quickly. However, there are two parts of errors in Learning-based methods: the fitting error of the network model and the making error of the prior dataset, which limits the potential for further improvements in focusing accuracy. In this paper, a high-precision autofocus pipeline was introduced, which predicts the defocus distance from a single natural image. A new method for making datasets was proposed, which overcomes the limitations of the sharpness metric itself and improves the overall accuracy of the dataset. Furthermore, a lightweight regression network was built, namely Natural-image Defocus Prediction Model (NDPM), to improve the focusing accuracy. A realistic dataset of sufficient size was made to train all models. The experiment shows NDPM has better focusing performance compared with other models, with a mean focusing error of 0.422µm.