Diffractive all-optical computing for quantitative phase imaging

TL;DR

This paper introduces a passive all-optical diffractive network for quantitative phase imaging that converts phase information into intensity variations, potentially replacing digital methods with a compact, power-efficient system.

Contribution

The work presents a novel diffractive all-optical network designed to perform phase-to-intensity transformation, reducing computational load and enabling high-speed, compact phase imaging.

Findings

Achieved a compact optical network extending 200-300 wavelengths.

Demonstrated the network's ability to synthesize phase images from input phase data.

Potential for high frame-rate, power-efficient phase imaging systems.

Abstract

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.