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.

中文翻译：

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AF-Net：一种用于相位检测自动对焦的卷积神经网络方法

对于自动对焦系统而言，准确而迅速地找到对焦镜头的位置非常重要，这样无需人工干预即可捕获清晰的图像。相位检测器已嵌入图像传感器中，以提高自动对焦的性能。然而，左右相位图像之间的相移估计对噪声敏感。在本文中，我们提出了一个基于卷积神经网络的鲁棒模型来解决这个问题。我们的模型包括四个卷积层，用于从相位图像中提取特征图；以及一个完全连接的网络，用于确定镜头移动。我们模型的最终镜头位置误差比最新的统计PDAF方法小五倍。此外，我们的模型在所有初始镜头位置上都能始终如一地运作良好。