# Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor arXiv: https://arxiv.org/abs/2403.11035 ยท HTML: https://mlsanity.com/paper/2403.11035/ ## 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 (most cited first, 30 of 30) - [All-Optical Machine Learning Using Diffractive Deep Neural Networks](https://mlsanity.com/paper/1804.08711/) (2018) - [Phase recovery and holographic image reconstruction using deep learning in neural networks](https://mlsanity.com/paper/1705.04286/) (2017) - [Recovery of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements using Neural Fields](https://mlsanity.com/paper/2112.00002/) (2021) - [PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning](https://mlsanity.com/paper/1807.07701/) (2018) - [Misalignment Resilient Diffractive Optical Networks](https://mlsanity.com/paper/2005.11464/) (2020) - [Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks](https://mlsanity.com/paper/1810.01916/) (2018) - [Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks](https://mlsanity.com/paper/1909.06553/) (2019) - [All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces](https://mlsanity.com/paper/2108.09833/) (2021) - [Diffractive all-optical computing for quantitative phase imaging](https://mlsanity.com/paper/2201.08964/) (2022) - [To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects](https://mlsanity.com/paper/2205.13122/) (2022) - [Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization](https://mlsanity.com/paper/2204.10533/) (2022) - [Cross-modality deep learning brings bright-field microscopy contrast to holography](https://mlsanity.com/paper/1811.07103/) (2018) - [Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors](https://mlsanity.com/paper/2303.13037/) (2023) - [Snapshot Multispectral Imaging Using a Diffractive Optical Network](https://mlsanity.com/paper/2212.05217/) (2022) - [Data class-specific all-optical transformations and encryption](https://mlsanity.com/paper/2212.12873/) (2022) - [Self-supervised learning of hologram reconstruction using physics consistency](https://mlsanity.com/paper/2209.08288/) (2022) - [Quantitative Phase Imaging and Artificial Intelligence: A Review](https://mlsanity.com/paper/1806.03982/) (2018) - [Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning](https://mlsanity.com/paper/2001.10695/) (2020) - [Unidirectional Imaging using Deep Learning-Designed Materials](https://mlsanity.com/paper/2212.02025/) (2022) - [Computer-free, all-optical reconstruction of holograms using diffractive networks](https://mlsanity.com/paper/2107.08177/) (2021) - [Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network](https://mlsanity.com/paper/2301.07908/) (2023) - [Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network](https://mlsanity.com/paper/2308.02952/) (2023) - [Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network](https://mlsanity.com/paper/2208.10362/) (2022) - [eFIN: Enhanced Fourier Imager Network for generalizable autofocusing and pixel super-resolution in holographic imaging](https://mlsanity.com/paper/2301.03162/) (2023) - [Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer](https://mlsanity.com/paper/2304.05724/) (2023) - [All-Optical Phase Conjugation Using Diffractive Wavefront Processing](https://mlsanity.com/paper/2311.04473/) (2023) - [Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning](https://mlsanity.com/paper/2007.10734/) (2020) - [Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning](https://mlsanity.com/paper/2207.00089/) (2022) - [Deep learning approach to coherent noise reduction in optical diffraction tomography](https://mlsanity.com/paper/1811.06950/) (2018) - [Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks](https://mlsanity.com/paper/2310.03384/) (2023) ## Built on by (most cited first, 2 of 2) - [Wavelength-multiplexed massively parallel diffractive optical information storage and image projection](https://mlsanity.com/paper/2604.02624/) (2026) - [Wavelength-Multiplexed 2D Beam Steering via a Passive Diffractive Network](https://mlsanity.com/paper/2606.16261/) (2026) ## 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.