Autofocus methods based on laser illumination

Abstract

Autofocusing system plays an important role in microscopic measurement. However, natural-image-based autofocus methods encounter difficulties in improving focusing accuracy and robustness due to the diversity of detection objects. In this paper, a high-precision autofocus method with laser illumination was proposed, termed laser split-image autofocus (LSA), which actively endows the detection scene with image features. The common non-learning-based and learning-based methods for LSA were quantitatively analyzed and evaluated. Furthermore, a lightweight comparative framework model for LSA, termed split-image comparison model (SCM), was proposed to further improve the focusing accuracy and robustness, and a realistic split-image dataset of sufficient size was made to train all models. The experiment showed LSA has better focusing performance than natural-image-based method. In addition, SCM has a great improvement in accuracy and robustness compared with previous learning and non-learning methods, with a mean focusing error of 0.317µm in complex scenes. Therefore, SCM is more suitable for industrial measurement.