2 citations · 4 across the 4 of their papers we have counts for
4 papers
Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems
Samuel Hurault, Ulugbek Kamilov, Arthur Leclaire +1
Plug-and-Play (PnP) methods are efficient iterative algorithms for solving ill-posed image inverse problems. PnP methods are obtained by using deep Gaussian denoisers instead of th…
Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems
Zihao Zou, Jiaming Liu, Brendt Wohlberg +1
There has been significant recent interest in the use of deep learning for regularizing imaging inverse problems. Most work in the area has focused on regularization imposed implic…
Deep Model-Based Architectures for Inverse Problems under Mismatched Priors
Shirin Shoushtari, Jiaming Liu, Yuyang Hu +1
There is a growing interest in deep model-based architectures (DMBAs) for solving imaging inverse problems by combining physical measurement models and learned image priors specifi…
Compressive Imaging with Iterative Forward Models
Hsiou-Yuan Liu, Ulugbek S. Kamilov, Dehong Liu +2
We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements. Our method relies on a novel, nonlinear measurement mo…