206 citations · 315 across the 8 of their papers we have counts for
5 papers · 1 filter
Local Conditional Neural Fields for Versatile and Generalizable Large-Scale Reconstructions in Computational Imaging
Hao Wang, Jiabei Zhu, Yunzhe Li +2
Deep learning has transformed computational imaging, but traditional pixel-based representations limit their ability to capture continuous, multiscale details of objects. Here we i…
Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples
Alex Matlock, Lei Tian
Recovering 3D phase features of complex, multiple-scattering biological samples traditionally sacrifices computational efficiency and processing time for physical model accuracy an…
SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors
Zihui Wu, Yu Sun, Alex Matlock +3
Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large d…
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…