activity
20182021
most citedReliable deep-learning-based phase imaging with uncertainty quantification

206 citations · 206 across the 2 of their papers we have counts for

collaborators

8 papers

physics.optics2021

Acousto-optic Ptychography

Moriya Rosenfeld, Daniel Doktofsky, Gil Weinberg +3

Acousto-optic imaging (AOI) enables optical-contrast imaging deep inside scattering samples via localized ultrasound-modulation of scattered light. While AOI allows optical investi…

physics.optics2020

Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network

Yuzhe Li, Shiyi Cheng, Yujia Xue +1

Coherent imaging through scatter is a challenging task in computational imaging. Both model-based and data-driven approaches have been explored to solve the inverse scattering prob…

physics.med-ph2020

Diffuser-based computational imaging funduscope

Yunzhe Li, Gregory N. McKay, Nicholas J. Durr +1

Poor access to eye care is a major global challenge that could be ameliorated by low-cost, portable, and easy-to-use diagnostic technologies. Diffuser-based imaging has the potenti…

physics.optics2019

High-speed in vitro intensity diffraction tomography

Jiaji Li, Alex Matlock, Yunzhe Li +3

We demonstrate a label-free, scan-free {\it intensity} diffraction tomography technique utilizing annular illumination (aIDT) to rapidly characterize large-volume 3D refractive ind…

eess.IV2019206 cited

Reliable deep-learning-based phase imaging with uncertainty quantification

Yujia Xue, Shiyi Cheng, Yunzhe Li +1

Emerging deep-learning (DL)-based techniques have significant potential to revolutionize biomedical imaging. However, one outstanding challenge is the lack of reliability assessmen…

cs.CV2018

Regularized Fourier Ptychography using an Online Plug-and-Play Algorithm

Yu Sun, Shiqi Xu, Yunzhe Li +3

The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser with…