1.1k citations · 2.1k across the 9 of their papers we have counts for
9 papers · 1 filter
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…
Label-free detection of Giardia lamblia cysts using a deep learning-enabled portable imaging flow cytometer
Zoltan Gorocs, David Baum, Fang Song +8
We report a field-portable and cost-effective imaging flow cytometer that uses deep learning to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput…
Misalignment Resilient Diffractive Optical Networks
Deniz Mengu, Yifan Zhao, Nezih T. Yardimci +3
As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light-matter intera…
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…