Journal Article

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

Ozcan, Aydogan, Mengu, Deniz, Shen, Che-Yung, Li, Jingxi

2023

Overview

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. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The 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 simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy. Leveraging deep learning‐designed diffractive layers, an all‐optical diffractive processor can rapidly obtain multispectral quantitative phase images (QPI) of transparent objects by transforming their phase profiles at target spectral bands into spatial intensity variations measured by a monochrome image sensor. This compact diffractive QPI framework can work at different parts of the spectrum through integration with various image sensors.

• Publisher: John Wiley & Sons, Inc,Wiley Blackwell (John Wiley & Sons),Wiley
• Subject: Arrays / Band spectra / Deep learning / Design / Design optimization / diffractive computing / diffractive networks / Focal plane devices / Imaging techniques / label-free imaging / Materials science / Microprocessors / Microscopy / multispectral imaging / Neural networks / Optical communication / optical processors / Power / quantitative phase imaging / Sensors / Spectral bands / Spectrum analysis / Visible spectrum