  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.
