Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
Che-Yung Shen, Jingxi Li, Deniz Mengu, Aydogan Ozcan

TL;DR
This paper introduces a novel all-optical multispectral quantitative phase imaging system using a diffractive neural network, enabling snapshot, label-free imaging across multiple wavelengths with high efficiency and broad applicability.
Contribution
The work presents a deep learning-optimized diffractive optical processor capable of performing multispectral QPI in a single snapshot, a significant advancement over traditional methods.
Findings
Successfully demonstrated multispectral QPI at 9 and 16 spectral bands.
Maintained uniform performance across all wavelength channels.
Validated generalization on unseen objects like Pap smear images.
Abstract
As a label-free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength-specific phase and dispersion characteristics of samples. Here, we present the design of a diffractive processor that can all-optically perform multispectral quantitative phase imaging of transparent phase-only objects in a snapshot. Our design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical…
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Taxonomy
TopicsDigital Holography and Microscopy · Optical Coherence Tomography Applications · Optical Polarization and Ellipsometry
