Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

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 simulations, we demonstrate diffractive multispectral processors to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. These diffractive multispectral processors maintain uniform performance across all the wavelength channels, revealing a decent QPI performance at each target wavelength. 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. This framework can operate at different parts of the electromagnetic spectrum and be used for a wide range of phase imaging and sensing applications.

arXiv comment29 Pages, 6 Figures

References in corpus (20)

• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• Phase recovery and holographic image reconstruction using deep learning in neural networks
• PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning
• Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
• Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
• Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
• Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning
• All-Optical Information Processing Capacity of Diffractive Surfaces
• Misalignment Resilient Diffractive Optical Networks
• Cross-modality deep learning brings bright-field microscopy contrast to holography
• Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
• Quantitative phase microscopy spatial signatures of cancer cells
• Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
• Snapshot Multispectral Imaging Using a Diffractive Optical Network
• Diffractive all-optical computing for quantitative phase imaging
• To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
• Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
• Data class-specific all-optical transformations and encryption
• Unidirectional Imaging using Deep Learning-Designed Materials
• eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging

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