530 citations · 1.2k across the 9 of their papers we have counts for
10 papers
Wide-Field, High-Resolution Reconstruction in Computational Multi-Aperture Miniscope Using a Fourier Neural Network
Qianwan Yang, Ruipeng Guo, Guorong Hu +3
Traditional fluorescence microscopy is constrained by inherent trade-offs among resolution, field-of-view, and system complexity. To navigate these challenges, we introduce a simpl…
Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network
Jeffrey Alido, Joseph Greene, Yujia Xue +7
Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scatteri…
Block Modulating Video Compression: An Ultra Low Complexity Image Compression Encoder for Resource Limited Platforms
Siming Zheng, Yujia Xue, Waleed Tahir +6
We consider the image and video compression on resource limited platforms. An ultra low-cost image encoder, named Block Modulating Video Compression (BMVC) with an encoding complex…
Deep-learning-augmented Computational Miniature Mesoscope
Yujia Xue, Qianwan Yang, Guorong Hu +2
Fluorescence microscopy is essential to study biological structures and dynamics. However, existing systems suffer from a tradeoff between field-of-view (FOV), resolution, and comp…
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…