206 citations · 206 across the 2 of their papers we have counts for
6 papers
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…
Single-Shot 3D Widefield Fluorescence Imaging with a Computational Miniature Mesoscope
Yujia Xue, Ian G. Davison, David A. Boas +1
Fluorescence imaging is indispensable to biology and neuroscience. The need for large-scale imaging in freely behaving animals has further driven the development in miniaturized mi…
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…
Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media
Yunzhe Li, Yujia Xue, Lei Tian
Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output "transmission matrix" for a fixe…
Deep learning approach to Fourier ptychographic microscopy
Thanh Nguyen, Yujia Xue, Yunzhe Li +2
Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct vid…
Hyperspectral Light Field Stereo Matching
Kang Zhu, Yujia Xue, Qiang Fu +3
In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a 5 x 6 array of cameras, with each…