210 citations · 378 across the 7 of their papers we have counts for
6 papers · 1 filter
On the interplay between physical and content priors in deep learning for computational imaging
Mo Deng, Shuai Li, Iksung Kang +2
Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two im…
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts
Mo Deng, Shuai Li, Alexandre Goy +2
The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the…
Low Photon Budget Phase Retrieval with Perceptual Loss Trained Deep Neural Networks
Mo Deng, Alexandre Goy, Shuai Li +2
Deep neural networks (DNNs) are efficient solvers for ill-posed problems and have been shown to outperform classical optimization techniques in several computational imaging proble…
High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networks
Alexandre Goy, Girish Rughoobur, Shuai Li +3
We present a Machine Learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to 10. Whereas previous ap…
Spectral pre-modulation of training examples enhances the spatial resolution of the Phase Extraction Neural Network (PhENN)
Shuai Li, George Barbastathis
The Phase Extraction Neural Network (PhENN) is a computational architecture, based on deep machine learning, for lens-less quantitative phase retrieval from raw intensity data. PhE…
Low Photon Count Phase Retrieval Using Deep Learning
Alexandre Goy, Kwabena Arthur, Shuai Li +1
Imaging systems' performance at low light intensity is affected by shot noise, which becomes increasingly strong as the power of the light source decreases. In this paper we experi…