The competition between resolution and the imaging field of view is a long-standing problem in traditional imaging systems — they can produce either an image of a small area with fine details or an image of a large area with coarse details. Fourier ptychography (FP) is an approach for tackling this intrinsic trade-off in imaging systems. It takes the challenge of high-throughput and high-resolution imaging from the domain of improving the physical limitations of optics to the domain of computation. It also enables post-measurement computational correction of optical aberrations. We present the basic concept of FP, compare it to related imaging modalities and then discuss experimental implementations, such as aperture-scanning FP, macroscopic camera-scanning FP, reflection mode, single-shot set-up, X-ray FP, speckle-scanning scheme and deep-learning-related implementations. Various applications of FP are discussed, including quantitative phase imaging in 2D and 3D, digital pathology, high-throughput cytometry, aberration metrology, long-range imaging and coherent X-ray nanoscopy. A collection of datasets and reconstruction codes is provided for readers interested in implementing FP themselves.

分辨率和成像视野之间的竞争是传统成像系统中的一个长期存在的问题-它们可以产生具有精细细节的小区域图像或具有粗糙细节的大区域图像。傅里叶指纹图谱（FP）是解决成像系统中这种固有折衷的方法。从提高光学器件的物理局限性到计算领域，它应对了高通量和高分辨率成像的挑战。它还可以进行光学像差的测量后计算校正。我们介绍FP的基本概念，将其与相关的成像方式进行比较，然后讨论实验性实现，例如光圈扫描FP，宏观相机扫描FP，反射模式，单次设置，X射线FP，斑点扫描方案和与深度学习相关的实现。讨论了FP的各种应用，包括2D和3D定量相成像，数字病理学，高通量细胞术，像差计量学，远距离成像和相干X射线纳米显微镜。为有兴趣实施FP的读者提供了数据集和重建代码的集合。