3 citations · 10 across the 9 of their papers we have counts for
11 papers
Atomic Depth Estimation From Noisy Electron Microscopy Data Via Deep Learning
Matan Leibovich, Mai Tan, Ramon Manzorro +4
We present a novel approach for extracting 3D atomic-level information from transmission electron microscopy (TEM) images affected by significant noise. The approach is based on fo…
Imaging with super-resolution in changing random media
Alexander Christie, Matan Leibovich, Miguel Moscoso +3
We develop an imaging algorithm that exploits strong scattering to achieve super-resolution in changing random media. The method processes large and diverse array datasets using sp…
An Analysis of Deep Learning Parameterizations for Ocean Subgrid Eddy Forcing
Cem Gultekin, Adam Subel, Cheng Zhang +5
Due to computational constraints, climate simulations cannot resolve a range of small-scale physical processes, which have a significant impact on the large-scale evolution of the…
Super-resolution in disordered media using neural networks
Alexander Christie, Matan Leibovich, Miguel Moscoso +3
We propose a methodology that exploits large and diverse data sets to accurately estimate the ambient medium's Green's functions in strongly scattering media. Given these estimates…
Atomic Resolution Observations of Nanoparticle Surface Dynamics and Instabilities Enabled by Artificial Intelligence
Peter A. Crozier, Matan Leibovich, Piyush Haluai +9
Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level proc…
Evaluating Unsupervised Denoising Requires Unsupervised Metrics
Adria Marcos-Morales, Matan Leibovich, Sreyas Mohan +5
Unsupervised denoising is a crucial challenge in real-world imaging applications. Unsupervised deep-learning methods have demonstrated impressive performance on benchmarks based on…
Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu +8
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained…
Correlation based Imaging for rotating satellites
Matan Leibovich, George Papanicolaou, Chrysoula Tsogka
We consider imaging of fast moving small objects in space, such as low earth orbit satellites, which are also rotating around a fixed axis. The imaging system consists of ground ba…
Generalized correlation based Imaging for satellites
Matan Leibovich, George Papanicolaou, Chrysoula Tsogka
We consider imaging of fast moving small objects in space, such as low earth orbit satellites. The imaging system consists of ground based, asynchronous sources of radiation and se…
Synthetic aperture imaging and motion estimation using tensor methods
Matan Leibovich, George Papanicolaou, Chrysoula Tsogka
We consider a synthetic aperture imaging configuration, such as synthetic aperture radar (SAR), where we want to first separate reflections from moving targets from those coming fr…
Low rank plus sparse decomposition of synthetic aperture radar data for target imaging and tracking
Matan Leibovich, George Papanicolaou, Chrysoula Tsogka
We analyze synthetic aperture radar (SAR) imaging of complex ground scenes that contain both stationary and moving targets. In the usual SAR acquisition scheme, we consider ways to…