393 citations · 1.4k across the 36 of their papers we have counts for
15 papers · 1 filter
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
Iksung Kang, Yi Jiang, Mirko Holler +5
Three-dimensional inspection of nanostructures such as integrated circuits is important for security and reliability assurance. Two scanning operations are required: ptychographic…
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
Iksung Kang, Ziling Wu, Yi Jiang +5
Noninvasive X-ray imaging of nanoscale three-dimensional objects, e.g. integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational an…
Noise-resilient approach for deep tomographic imaging
Zhen Guo, Zhiguang Liu, Qihang Zhang +2
We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantage…
AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing
Fabian A. Braeu, Thanadet Chuangsuwanich, Tin A. Tun +4
: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH rob…
Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis
Fabian A. Braeu, Alexandre H. Thiéry, Tin A. Tun +4
Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is th…
Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
Qihang Zhang, Janaka C. Gamekkanda, Ajinkya Pandit +9
Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon pr…
Physics-assisted Generative Adversarial Network for X-Ray Tomography
Zhen Guo, Jung Ki Song, George Barbastathis +5
X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection…
Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography
Zhen Guo, Jung Ki Song, George Barbastathis +5
Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in…
Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
Iksung Kang, Alexandre Goy, George Barbastathis
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging,…
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…