2.6k citations · 5.2k across the 30 of their papers we have counts for
30 papers
Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array
Dehui Zhang, Dong Xu, Yuhang Li +14
Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under w…
Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network
Yuhang Li, Yi Luo, Deniz Mengu +2
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 o…
Virtual impactor-based label-free bio-aerosol detection using holography and deep learning
Yi Luo, Yijie Zhang, Tairan Liu +3
Exposure to bio-aerosols such as mold spores and pollen can lead to adverse health effects. There is a need for a portable and cost-effective device for long-term monitoring and qu…
Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
Jingxi Li, Bijie Bai, Yi Luo +1
We report deep learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily-selected, complex-valued…
All-optical image classification through unknown random diffusers using a single-pixel diffractive network
Yi Luo, Bijie Bai, Yuhang Li +2
Classification of an object behind a random and unknown scattering medium sets a challenging task for computational imaging and machine vision fields. Recent deep learning-based ap…
Super-resolution image display using diffractive decoders
Cagatay Isil, Deniz Mengu, Yifan Zhao +5
High-resolution synthesis/projection of images over a large field-of-view (FOV) is hindered by the restricted space-bandwidth-product (SBP) of wavefront modulators. We report a dee…
To image, or not to image: Class-specific diffractive cameras with all-optical erasure of undesired objects
Bijie Bai, Yi Luo, Tianyi Gan +6
Privacy protection is a growing concern in the digital era, with machine vision techniques widely used throughout public and private settings. Existing methods address this growing…
Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers
Yuhang Li, Yi Luo, Bijie Bai +1
Imaging through diffusive media is a challenging problem, where the existing solutions heavily rely on digital computers to reconstruct distorted images. We provide a detailed anal…
Cascadable all-optical NAND gates using diffractive networks
Yi Luo, Deniz Mengu, Aydogan Ozcan
Owing to its potential advantages such as scalability, low latency and power efficiency, optical computing has seen rapid advances over the last decades. A core unit of a potential…
Characterization of exhaled e-cigarette aerosols in a vape shop using a field-portable holographic on-chip microscope
Ege Cetintas, Yi Luo, Charlene Nguyen +4
The past decade marked a drastic increase in the usage of electronic cigarettes (e-cigs). The adverse health impact of secondhand exposure due to exhaled e-cig particles has raised…
Computational Imaging Without a Computer: Seeing Through Random Diffusers at the Speed of Light
Yi Luo, Yifan Zhao, Jingxi Li +4
Imaging through diffusers presents a challenging problem with various digital image reconstruction solutions demonstrated to date using computers. We present a computer-free, all-o…
Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning
Yi Luo, Hyou-Arm Joung, Sarah Esparza +3
Particle agglutination assays are widely adapted immunological tests that are based on antigen-antibody interactions. Antibody-coated microscopic particles are mixed with a test sa…
Dynamic imaging and characterization of volatile aerosols in e-cigarette emissions using deep learning-based holographic microscopy
Yi Luo, Yichen Wu, Liqiao Li +4
Various volatile aerosols have been associated with adverse health effects; however, characterization of these aerosols is challenging due to their dynamic nature. Here we present…
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
Yijie Zhang, Tairan Liu, Manmohan Singh +4
Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning…
Holographic image reconstruction with phase recovery and autofocusing using recurrent neural networks
Luzhe Huang, Tairan Liu, Xilin Yang +3
Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important st…
Deep learning-based virtual refocusing of images using an engineered point-spread function
Xilin Yang, Luzhe Huang, Yilin Luo +4
We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF). This netwo…
Recurrent neural network-based volumetric fluorescence microscopy
Luzhe Huang, Yilin Luo, Yair Rivenson +1
Volumetric imaging of samples using fluorescence microscopy plays an important role in various fields including physical, medical and life sciences. Here we report a deep learning-…
Deep learning-based holographic polarization microscopy
Tairan Liu, Kevin de Haan, Bijie Bai +8
Polarized light microscopy provides high contrast to birefringent specimen and is widely used as a diagnostic tool in pathology. However, polarization microscopy systems typically…
Terahertz Pulse Shaping Using Diffractive Surfaces
Muhammed Veli, Deniz Mengu, Nezih T. Yardimci +5
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive…
Spectrally-Encoded Single-Pixel Machine Vision Using Diffractive Networks
Jingxi Li, Deniz Mengu, Nezih T. Yardimci +6
3D engineering of matter has opened up new avenues for designing systems that can perform various computational tasks through light-matter interaction. Here, we demonstrate the des…
Single-shot autofocusing of microscopy images using deep learning
Yilin Luo, Luzhe Huang, Yair Rivenson +1
We demonstrate a deep learning-based offline autofocusing method, termed Deep-R, that is trained to rapidly and blindly autofocus a single-shot microscopy image of a specimen that…
Design of Task-Specific Optical Systems Using Broadband Diffractive Neural Networks
Yi Luo, Deniz Mengu, Nezih T. Yardimci +4
We report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally-incoherent broadband source to al…
Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy
Jingxi Li, Deniz Mengu, Yi Luo +2
Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all…
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Yichen Wu, Yair Rivenson, Hongda Wang +5
Three-dimensional (3D) fluorescence microscopy in general requires axial scanning to capture images of a sample at different planes. Here we demonstrate that a deep convolutional n…