论文标题
智能相机
Smart Cameras
论文作者
论文摘要
我们回顾人工智能时代的相机架构。现代相机使用物理组件和软件来捕获，压缩和显示图像数据。在过去的五年中，深度学习解决方案已经优于这些功能的传统算法。深度学习可以使每个像素的电气传感器功率减少10-100倍，景深和动态范围的深度提高了10倍，图像像素计数的提高了10-100倍。深度学习实现了从根本上改变物理摄像头设计目标的多帧和多局部解决方案。在这里，我们回顾了相机操作中深度学习的艺术状况，并考虑AI对相机物理设计的影响。
We review camera architecture in the age of artificial intelligence. Modern cameras use physical components and software to capture, compress and display image data. Over the past 5 years, deep learning solutions have become superior to traditional algorithms for each of these functions. Deep learning enables 10-100x reduction in electrical sensor power per pixel, 10x improvement in depth of field and dynamic range and 10-100x improvement in image pixel count. Deep learning enables multiframe and multiaperture solutions that fundamentally shift the goals of physical camera design. Here we review the state of the art of deep learning in camera operations and consider the impact of AI on the physical design of cameras.
