Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network

Abstract

Quantitative phase imaging (QPI) is a label-free computational imaging technique used in various fields, including biology and medical research. Modern QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image reconstruction. Here, we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane, all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown, random phase diffusers. This QPI diffractive network is composed of successive diffractive layers, axially spanning in total ~70 wavelengths; unlike existing digital image reconstruction and phase retrieval methods, it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light propagation. This all-optical diffractive processor can provide a low-power, high frame rate and compact alternative for quantitative imaging of phase objects through random, unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and sensing. The presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope, performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.

arXiv comment27 Pages, 7 Figures

References in corpus (22)

• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• Non-invasive real-time imaging through scattering layers and around corners via speckle correlations
• 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
• Analysis of Diffractive Optical Neural Networks and Their Integration with Electronic Neural Networks
• Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
• Reliable deep-learning-based phase imaging with uncertainty quantification
• Terahertz Pulse Shaping Using Diffractive Surfaces
• All-Optical Information Processing Capacity of Diffractive Surfaces
• Misalignment Resilient Diffractive Optical Networks
• Cross-modality deep learning brings bright-field microscopy contrast to holography
• Diffractive all-optical computing for quantitative phase imaging
• High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networks
• Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors
• Super-resolution image display using diffractive decoders
• Imaging of objects through a thin scattering layer using a spectrally and spatially separated reference
• Imaging through a thin scattering layer and jointly retrieving the point-spread-function using phase-diversity
• Classification and reconstruction of spatially overlapping phase images using diffractive optical networks
• Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
• Computer-free, all-optical reconstruction of holograms using diffractive networks
• All-Optical Synthesis of an Arbitrary Linear Transformation Using Diffractive Surfaces

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• All-optical image denoising using a diffractive visual processor
• Multiplane Quantitative Phase Imaging Using a Wavelength-Multiplexed Diffractive Optical Processor
• Pyramid diffractive optical networks for unidirectional image magnification and demagnification
• Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
• Unidirectional imaging with partially coherent light
• Phase-multiplexed optical computing: Reconfiguring a multi-task diffractive optical processor using illumination phase diversity