Optical imaging and sensing systems based on diffractive elements have seen massive advances over the last several decades. Earlier generations of diffractive optical processors were, in general, designed to deliver information to an independent system that was separately optimized, primarily driven by human vision or perception. With the recent advances in deep learning and digital neural networks, there have been efforts to establish diffractive processors that are jointly optimized with digital neural networks serving as their back-end. These jointly optimized hybrid (optical+digital) processors establish a new "diffractive language" between input electromagnetic waves that carry analog information and neural networks that process the digitized information at the back-end, providing the best of both worlds. Such hybrid designs can process spatially and temporally coherent, partially coherent, or incoherent input waves, providing universal coverage for any spatially varying set of point spread functions that can be optimized for a given task, executed in collaboration with digital neural networks. In this article, we highlight the utility of this exciting collaboration between engineered and programmed diffraction and digital neural networks for a diverse range of applications. We survey some of the major innovations enabled by the push-pull relationship between analog wave processing and digital neural networks, also covering the significant benefits that could be reaped through the synergy between these two complementary paradigms.

翻译：基于衍射元件的光学成像与传感系统在过去几十年取得了巨大进展。早期的衍射光学处理器通常设计为向独立优化的系统传递信息，主要服务于人类视觉或感知。随着深度学习和数字神经网络的最新进展，学界开始致力于构建与后端数字神经网络联合优化的衍射处理器。这些联合优化的混合（光学+数字）处理器在携带模拟信息的输入电磁波与后端处理数字化信息的神经网络之间建立了一种新的“衍射语言”，实现了两者的优势互补。此类混合设计能够处理空间和时间相干、部分相干或非相干的输入波，为任何可通过数字神经网络协同优化、针对特定任务执行的空间变化点扩散函数集合提供通用覆盖。本文重点阐述了工程化/可编程衍射与数字神经网络之间这种激动人心的协同作用在多样化应用中的效用。我们综述了模拟波处理与数字神经网络之间推拉关系所催生的重大创新，并涵盖这两种互补范式协同作用可能带来的显著优势。