3 citations · 5 across the 10 of their papers we have counts for
14 papers
Uncertainty Quantification for Deep Unrolling-Based Computational Imaging
Canberk Ekmekci, Mujdat Cetin
Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction meth…
Combining physics-based modeling and deep learning for ultrasound elastography
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Ultrasound elasticity images which enable the visualization of quantitative maps of tissue stiffness can be reconstructed by solving an inverse problem. Classical model-based appro…
Regularization by Adversarial Learning for Ultrasound Elasticity Imaging
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Classical model-based imaging methods for ultrasound elasticity inverse problem require prior constraints about the underlying elasticity patterns, while finding the appropriate ha…
Finite Element Reconstruction Of Stiffness Images In MR Elastography Using Statistical Physical Forward Modeling And Proximal Optimization Methods
Narges Mohammadi, Marvin M. Doyley, Mujdat Cetin
Quantitative characterization of tissue properties, known as elasticity imaging, can be cast as solving an ill-posed inverse problem. The finite element methods (FEMs) in magnetic…
Online Graph Learning under Smoothness Priors
Seyed Saman Saboksayr, Gonzalo Mateos, Mujdat Cetin
The growing success of graph signal processing (GSP) approaches relies heavily on prior identification of a graph over which network data admit certain regularity. However, adaptat…
Depth extraction from a single compressive hologram
Baturay Ozgurun, Mujdat Cetin
We propose a novel method that records a single compressive hologram in a short time and extracts the depth of a scene from that hologram using a stereo disparity technique. The me…