Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 17 of its 47 references that are in the map. First posted on arXiv 5 Aug 2023.
Builds on
17 of 47 references.
References · 17
- 2018All-Optical Machine Learning Using Diffractive Deep Neural Networkscited by 177
- 2017Phase recovery and holographic image reconstruction using deep learning in neural networkscited by 54
- 2018PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learningcited by 41
- 2020Misalignment Resilient Diffractive Optical Networkscited by 33
- 2019Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networkscited by 27
- 2021All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfacescited by 24
- 2022Diffractive all-optical computing for quantitative phase imagingcited by 22
- 2022Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalizationcited by 18
- 2018Cross-modality deep learning brings bright-field microscopy contrast to holographycited by 17
- 2023Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processorscited by 17
- 2022Snapshot Multispectral Imaging Using a Diffractive Optical Networkcited by 16
- 2022Data class-specific all-optical transformations and encryptioncited by 16
5 more
- 2020Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learningcited by 13
- 2022Unidirectional Imaging using Deep Learning-Designed Materialscited by 13
- 2021Computer-free, all-optical reconstruction of holograms using diffractive networkscited by 12
- 2022Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Networkcited by 10
- 2022Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networkscited by 6
Built on by
Citing papers per year: 2023: 3; 2024: 5; 2025: 0; 2026: 2.
10 papers.
- 2023All-Optical Phase Conjugation Using Diffractive Wavefront Processingcited by 8
- 2023All-optical image denoising using a diffractive visual processorcited by 6
- 2023Pyramid diffractive optical networks for unidirectional image magnification and demagnificationcited by 3
- 2024Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processorcited by 2
- 2024All-optical complex field imaging using diffractive processorscited by 2
- 2024Multiplexed all-optical permutation operations using a reconfigurable diffractive optical networkcited by 2
- 2024Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processorcited by 2
- 2024Integration of Programmable Diffraction with Digital Neural Networkscited by 1
- 2026Wavelength-multiplexed massively parallel diffractive optical information storage and image projectioncited by 0
- 2026Wavelength-Multiplexed 2D Beam Steering via a Passive Diffractive Networkcited by 0
- 2026Wavelength-Multiplexed 2D Beam Steering via a Passive Diffractive Network15 Jun 2026
- 2026Wavelength-multiplexed massively parallel diffractive optical information storage and image projection3 Apr 2026
- 2024Integration of Programmable Diffraction with Digital Neural Networks15 Jun 2024
- 2024Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor16 Mar 2024
- 2024Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network4 Feb 2024
- 2024All-optical complex field imaging using diffractive processors30 Jan 2024
- 2024Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor17 Jan 2024
- 2023All-Optical Phase Conjugation Using Diffractive Wavefront Processing8 Nov 2023
- 2023All-optical image denoising using a diffractive visual processor17 Sep 2023
- 2023Pyramid diffractive optical networks for unidirectional image magnification and demagnification29 Aug 2023