Training A Phase Detection Autofocus Model Using Hybrid Labels

Phase detection autofocus (PDAF) technology is essential to digital cameras in many applications including professional photography and autonomous navigation. A deep-learning-based autofocus method is advantageous over conventional AF methods in both speed and accuracy; however, the quality of training data remains a major bottleneck. In this paper, we propose a hybrid labeling strategy that leverages the complementary strengths of focus profiles derived from phase and RGB data. The latter has finer spatial resolution but is noisier than the former. By including both types of data for model training, better accuracy and reliability for PDAF can be achieved. Experiments on various scenes demonstrate that the proposed hybrid labeling strategy achieves higher accuracy than monotype labeling strategies, leading to a practical single-camera alternative to multi-camera-based or depth-based labeling solutions that are often clumsy and computationally expensive.