An infrastructure for analysing the performance of computational imaging algorithms on modern hardware

许多应用，如医学成像、电影制作、汽车和机器人等，都使用成像传感器(IS)将光转换为适合数字设备进一步处理和存储的信号。IS的输出并不完美，需要在数字域中进行大量处理才能产生可接受的结果。例如，镜头缺陷会导致输出失真，而传感器灵敏度可能会产生曝光不足或曝光过度的输出，从而遗漏关键信息。计算成像(CI)是在数字域进行处理的IS输出，可以弥补这些局限性。然而，CI复杂性的进一步发展受到硬件性能的严重限制。具体地说，在每个计算设备的核心，都有一个或多个处理核心来处理数据值，并在存储设备或与物理世界交互的设备(如显示器和触摸屏)之间移动它们。现有的通用处理核心跟不上CI应用程序对计算能力的需求。几十年来，核心绩效每两年翻一番。核心绩效是指数据价值可以被操纵或转移的速度。这使得应用程序的复杂性和广度都有了进步。应用程序开发人员可以依靠这一性能提升的趋势，提前计划创新以

Numerous applications, such as those in medical imaging, film production, automotive and robotics, use imaging sensors (IS) to convert light to signals appropriate for further processing and storage by digital devices. IS output is imperfect and requires significant processing in the digital domain to yield acceptable results. For example, lens imperfections result in distorted output, while sensor sensitivity may yield output that is underexposed or overexposed at places and thus missing crucial information. Computational Imaging (CI) is the processing in the digital domain of IS output can compensate for these limitations. However, further advances in CI sophistication are severely limited by hardware performance. Specifically, at the heart of every computing device, there are one or more processing cores that manipulate data values and move them among storage devices or devices that interact with the physical world such as displays and touch screens. Existing general purpose processing cores are not keeping pace with the computational power demands of CI applications. For several decades, core performance, that is the rate at which data values could be manipulated or moved around, has doubled every two years. This has enabled advances in application sophistication and breadth. Application developers could rely on this trend of performance increase and plan ahead innovations to