Diffractive all-optical computing for quantitative phase imaging
This could enable power-efficient, high frame-rate, and compact phase imaging systems for applications like on-chip microscopy and sensing, representing a novel method for a known bottleneck.
The authors tackled the computational burden of quantitative phase imaging by designing a diffractive all-optical network that converts phase information into intensity variations, achieving a compact system extending only ~200-300 times the illumination wavelength.
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.