Most digital cameras use specialized autofocus sensors, such as phase detection, lidar or ultrasound, to directly measure focus state. However, such sensors increase cost and complexity without directly optimizing final image quality. This paper proposes a new pipeline for image-based autofocus and shows that neural image analysis finds focus 5-10x faster than traditional contrast enhancement. We achieve this by learning the direct mapping between an image and its focus position. In further contrast with conventional methods, AI methods can generate scene-based focus trajectories that optimize synthesized image quality for dynamic and three dimensional scenes. We propose a focus control strategy that varies focal position dynamically to maximize image quality as estimated from the focal stack. We propose a rule-based agent and a learned agent for different scenarios and show their advantages over other focus stacking methods.

中文翻译：

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相机自动对焦的深度学习

大多数数码相机都使用专门的自动对焦传感器（例如相位检测，激光雷达或超声波）直接测量对焦状态。但是，这种传感器增加了成本和复杂性，而没有直接优化最终图像质量。本文提出了一种新的基于图像的自动对焦流程，并表明神经图像分析发现的对焦速度比传统的对比度增强快5-10倍。我们通过学习图像与其焦点位置之间的直接映射来实现这一点。与传统方法形成鲜明对比的是，AI方法可以生成基于场景的焦点轨迹，从而优化动态和三维场景的合成图像质量。我们提出了一种焦点控制策略，该策略可以动态更改焦点位置，以最大程度地提高从焦点堆栈估算的图像质量。