mlsanityData to 14 Oct 2026

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

arXiv 2308.02952 ↗First posted 5 Aug 2023physics.optics cs.CV physics.app-phcited by 10

Not on the map. Why?

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

  1. 2018All-Optical Machine Learning Using Diffractive Deep Neural Networkscited by 177
  2. 2017Phase recovery and holographic image reconstruction using deep learning in neural networkscited by 54
  3. 2018PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learningcited by 41
  4. 2020Misalignment Resilient Diffractive Optical Networkscited by 33
  5. 2019Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networkscited by 27
  6. 2021All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfacescited by 24
  7. 2022Diffractive all-optical computing for quantitative phase imagingcited by 22
  8. 2022Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalizationcited by 18
  9. 2018Cross-modality deep learning brings bright-field microscopy contrast to holographycited by 17
  10. 2023Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processorscited by 17
  11. 2022Snapshot Multispectral Imaging Using a Diffractive Optical Networkcited by 16
  12. 2022Data class-specific all-optical transformations and encryptioncited by 16
5 more
  1. 2020Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learningcited by 13
  2. 2022Unidirectional Imaging using Deep Learning-Designed Materialscited by 13
  3. 2021Computer-free, all-optical reconstruction of holograms using diffractive networkscited by 12
  4. 2022Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Networkcited by 10
  5. 2022Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networkscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 0; 2026: 2.

10 papers.