Optical Model-Driven Sharpness Mapping for Autofocus in Small Depth-of-Field and Severe Defocus Scenarios

Chen-Liang Fan, Mingpei Cao, Chih Chien Hung, Yuesheng Zhu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 6426-6435

Autofocus (AF) is essential for imaging systems, particularly in industrial applications such as automated optical inspection (AOI), where achieving precise focus is critical. Conventional AF methods rely on peak-searching algorithms that require dense focal sampling, making them inefficient in small depth-of-field (DoF) scenarios. Deep learning (DL)-based AF methods, while effective in general imaging, have a limited working range in small DoF conditions due to defocus uncertainty. In this work, we propose a novel AF framework that integrates an optical model-based sharpness indicator with a deep learning approach to predict sharpness from defocused images. We leverage sharpness estimation as a reliable focus measure and apply an adaptive adjustment algorithm to adjust the focus position based on the sharpness-to-distance mapping. This method effectively addresses defocus uncertainty and enables robust autofocus across a 35x DoF range.Experimental results on an AOI system demonstrate that our approach achieves reliable autofocus even from highly defocused starting points and remains robust across different textures and illumination conditions. Compared to conventional and existing DL-based approaches, our method offers improved precision, efficiency, and adaptability, making it suitable for industrial applications and small DoF scenarios.