论文标题
物理与学习的先验：重新思考摄像头和特定任务成像的算法设计
Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging
论文作者
论文摘要
相机最初是使用基于物理的启发式方法来捕获美学图像的。近年来，相机设计从纯粹由物理驱动到越来越多的数据驱动和特定于任务的转变。在本文中，我们提出了一个框架，以了解相机硬件和算法的端到端设计的新生字段的基础。作为此框架的一部分，我们展示了如何利用物理和数据的方法在成像和计算机视觉中变得普遍，并强调了一个关键趋势，该趋势将继续统治特定于任务的相机设计的未来。最后，我们在端到端设计方面具有当前的进展障碍，并假设如何克服这些障碍。
Cameras were originally designed using physics-based heuristics to capture aesthetic images. In recent years, there has been a transformation in camera design from being purely physics-driven to increasingly data-driven and task-specific. In this paper, we present a framework to understand the building blocks of this nascent field of end-to-end design of camera hardware and algorithms. As part of this framework, we show how methods that exploit both physics and data have become prevalent in imaging and computer vision, underscoring a key trend that will continue to dominate the future of task-specific camera design. Finally, we share current barriers to progress in end-to-end design, and hypothesize how these barriers can be overcome.
