论文标题
定量相成像的衍射全光计算
Diffractive all-optical computing for quantitative phase imaging
论文作者
论文摘要
定量相成像（QPI）是一种无标签的计算成像技术，可提供样品的光路长度信息。在现代实现中，对象的定量相位图像是通过在计算机中运行的数值方法重建的，通常使用迭代算法。在这里，我们演示了一个衍射QPI网络，该网络可以通过将场景的输入相信息转换为输出平面的强度变化来综合对象的定量相位图像。衍射QPI网络是一种专业的全光处理器，旨在通过被动衍射表面进行定量的相位到强度转换，这些衍射表面是使用深度学习和图像数据进行空间设计的。该框架形成一个紧凑的全光网络，该网络仅扩展了照明波长的200-300倍，可以用一组被动的传播层来替换传统的QPI系统和相关的数字计算负担。全光衍射QPI网络可以潜在地启用功率高，高框架和紧凑的相成像系统，这些系统可能对各种应用有用，例如，例如，芯片显微镜和感应。
Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of specimens. In modern implementations, the quantitative phase image of an object is reconstructed digitally through numerical methods running in a computer, often using iterative algorithms. Here, we demonstrate a diffractive QPI network that can synthesize the quantitative phase image of an object by converting the input phase information of a scene into intensity variations at the output plane. A diffractive QPI network is a specialized all-optical processor designed to perform a quantitative phase-to-intensity transformation through passive diffractive surfaces that are spatially engineered using deep learning and image data. Forming a compact, all-optical network that axially extends only ~200-300 times the illumination wavelength, this framework can replace traditional QPI systems and related digital computational burden with a set of passive transmissive layers. All-optical diffractive QPI networks can potentially enable power-efficient, high frame-rate and compact phase imaging systems that might be useful for various applications, including, e.g., on-chip microscopy and sensing.
