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All-Optical Phase Recovery: Diffractive Computing for Quantitative Phase Imaging
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DOI：10.1002/adom.202200281.png)
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Abstract
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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, a diffractive QPI network that can perform all-optical phase recovery is demonstrated, and the quantitative phase image of an object is synthesized 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 approximate to 200-300 lambda, where lambda is 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., microscopy and sensing.
Keywords:
diffractive optical networks
deep learning
holography
light-matter interaction
optical computing
optical machine learning
quantitative phase imaging (QPI)
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