Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
Not on the map. Why?
Abstract (arXiv)
Quantitative phase imaging (QPI) is a label-free technique that provides optical path length information for transparent specimens, finding utility in biology, materials science, and engineering. Here, we present quantitative phase imaging of a 3D stack of phase-only objects using a wavelength-multiplexed diffractive optical processor. Utilizing multiple spatially engineered diffractive layers trained through deep learning, this diffractive processor can transform the phase distributions of multiple 2D objects at various axial positions into intensity patterns, each encoded at a unique wavelength channel. These wavelength-multiplexed patterns are projected onto a single field-of-view (FOV) at the output plane of the diffractive processor, enabling the capture of quantitative phase distributions of input objects located at different axial planes using an intensity-only image sensor. Based on numerical simulations, we show that our diffractive processor could simultaneously achieve all-optical quantitative phase imaging across several distinct axial planes at the input by scanning the illumination wavelength. A proof-of-concept experiment with a 3D-fabricated diffractive processor further validated our approach, showcasing successful imaging of two distinct phase objects at different axial positions by scanning the illumination wavelength in the terahertz spectrum. Diffractive network-based multiplane QPI designs can open up new avenues for compact on-chip phase imaging and sensing devices.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 82 references that are in the map. First posted on arXiv 16 Mar 2024.
Builds on
30 of 82 references.
References · 30
- 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
- 2021Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fieldscited by 48
- 2018PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learningcited by 41
- 2020Misalignment Resilient Diffractive Optical Networkscited by 33
- 2018Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networkscited by 30
- 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
- 2022To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objectscited by 21
- 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
18 more
- 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
- 2022Self-supervised learning of hologram reconstruction using physics consistencycited by 14
- 2018Quantitative Phase Imaging and Artificial Intelligence: A Reviewcited by 13
- 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
- 2023Quantitative phase imaging (QPI) through random diffusers using a diffractive optical networkcited by 10
- 2023Multispectral Quantitative Phase Imaging Using a Diffractive Optical Networkcited by 10
- 2022Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Networkcited by 9
- 2023eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imagingcited by 8
- 2023Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformercited by 8
- 2023All-Optical Phase Conjugation Using Diffractive Wavefront Processingcited by 8
- 2020Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learningcited by 6
- 2022Rapid and stain-free quantification of viral plaque via lens-free holography and deep learningcited by 6
- 2018Deep learning approach to coherent noise reduction in optical diffraction tomographycited by 5
- 2023Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networkscited by 4
Built on by
Citing papers per year: 2026: 2.
2 papers.