Diffractive all-optical computing for quantitative phase imaging

Abstract

Quantitative phase imaging (QPI) is a label-free computational imaging technique that provides optical path length information of specimens. In modern implementations, the quantitative phase image of an object is reconstructed digitally through numerical methods running in a computer, often using iterative algorithms. Here, we demonstrate a diffractive QPI network that can synthesize the quantitative phase image of an object by converting the input phase information of a scene into intensity variations at the output plane. A diffractive QPI network is a specialized all-optical processor designed to perform a quantitative phase-to-intensity transformation through passive diffractive surfaces that are spatially engineered using deep learning and image data. Forming a compact, all-optical network that axially extends only ~200-300 times the illumination wavelength, this framework can replace traditional QPI systems and related digital computational burden with a set of passive transmissive layers. All-optical diffractive QPI networks can potentially enable power-efficient, high frame-rate and compact phase imaging systems that might be useful for various applications, including, e.g., on-chip microscopy and sensing.

arXiv comment23 Pages, 5 Figures

References in corpus (12)

• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• Phase recovery and holographic image reconstruction using deep learning in neural networks
• 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
• Deep learning approach to Fourier ptychographic microscopy
• Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
• Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
• All-Optical Information Processing Capacity of Diffractive Surfaces
• Misalignment Resilient Diffractive Optical Networks
• Deep learning-based super-resolution in coherent imaging systems
• Quantitative phase microscopy spatial signatures of cancer cells
• Magnified Image Spatial Spectrum (MISS) microscopy for nanometer and millisecond scale label-free imaging

Cited by in corpus (29)

• On the use of deep learning for phase recovery
• Polarization Multiplexed Diffractive Computing: All-Optical Implementation of a Group of Linear Transformations Through a Polarization-Encoded Diffractive Network
• Snapshot Multispectral Imaging Using a Diffractive Optical Network
• Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
• All-optical image classification through unknown random diffusers using a single-pixel diffractive network
• Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
• All-optical image denoising using a diffractive visual processor
• Data class-specific all-optical transformations and encryption
• Unidirectional Imaging using Deep Learning-Designed Materials
• Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
• All-optical complex field imaging using diffractive processors
• Universal Polarization Transformations: Spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer
• Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
• Simultaneously sorting vector vortex beams of 120 modes
• Pyramid diffractive optical networks for unidirectional image magnification and demagnification
• Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network
• Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks
• Time-lapse image classification using a diffractive neural network
• All-Optical Phase Conjugation Using Diffractive Wavefront Processing
• Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding
• Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
• Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
• Integration of Programmable Diffraction with Digital Neural Networks
• Optoelectronic generative adversarial networks
• Universal point spread function engineering for 3D optical information processing
• Broadband Unidirectional Visible Imaging Using Wafer-Scale Nano-Fabrication of Multi-Layer Diffractive Optical Processors
• Coherence Awareness in Diffractive Neural Networks
• Diffractive Interconnects: All-Optical Permutation Operation Using Diffractive Networks
• Phase-multiplexed optical computing: Reconfiguring a multi-task diffractive optical processor using illumination phase diversity