AF-Net: A Convolutional Neural Network Approach to Phase Detection Autofocus
Published in IEEE Transactions on Image Processing, 2020
Recommended citation: Chi-Jui Ho, Chin-Cheng Chan and Homer H. Chen, "AF-Net: A Convolutional Neural Network Approach to Phase Detection Autofocus," in IEEE Transactions on Image Processing, vol. 29, pp. 6386-6395, 2020, doi: 10.1109/TIP.2019.2947349.
Abstract: It is important for an autofocus system to accurately and quickly find the in-focus lens position so that sharp images can be captured without human intervention. Phase detectors have been embedded in image sensors to improve the performance of autofocus; however, the phase shift estimation between the left and right phase images is sensitive to noise. In this paper, we propose a robust model based on convolutional neural network to address this issue. Our model includes four convolutional layers to extract feature maps from the phase images and a fully-connected network to determine the lens movement. The final lens position error of our model is five times smaller than that of a state-of-the-art statistical PDAF method. Furthermore, our model works consistently well for all initial lens positions. All these results verify the robustness of our model.