56 citations · 118 across the 11 of their papers we have counts for
11 papers
Constrained Regularization by Denoising with Automatic Parameter Selection
Pasquale Cascarano, Alessandro Benfenati, Ulugbek S. Kamilov +1
Regularization by Denoising (RED) is a well-known method for solving image restoration problems by using learned image denoisers as priors. Since the regularization parameter in th…
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior
Zongyu Li, Jason Hu, Xiaojian Xu +2
Phase retrieval (PR) is a crucial problem in many imaging applications. This study focuses on resolving the holographic phase retrieval problem in situations where the measurements…
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
Xiaojian Xu, Weijie Gan, Satya V. V. N. Kothapalli +2
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately…
Online Deep Equilibrium Learning for Regularization by Denoising
Jiaming Liu, Xiaojian Xu, Weijie Gan +2
Plug-and-Play Priors (PnP) and Regularization by Denoising (RED) are widely-used frameworks for solving imaging inverse problems by computing fixed-points of operators combining ph…
Monotonically Convergent Regularization by Denoising
Yuyang Hu, Jiaming Liu, Xiaojian Xu +1
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors. Recent work has reported the state-of-the-a…
Bregman Plug-and-Play Priors
Abdullah H. Al-Shabili, Xiaojian Xu, Ivan Selesnick +1
The past few years have seen a surge of activity around integration of deep learning networks and optimization algorithms for solving inverse problems. Recent work on plug-and-play…
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative Mapping
Xiaojian Xu, Satya V. V. N. Kothapalli, Jiaming Liu +4
Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and -inhomogeneity-corrected maps…
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Jiaming Liu, Yu Sun, Weijie Gan +3
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems. However, the computational and memory complexity of data-consistency lay…
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Xiaojian Xu, Yu Sun, Jiaming Liu +2
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser. While PnP algorithms are well understood for de…
Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling
Xiaojian Xu, Jiaming Liu, Yu Sun +2
Plug-and-play priors (PnP) is an image reconstruction framework that uses an image denoiser as an imaging prior. Unlike traditional regularized inversion, PnP does not require the…
Image Restoration using Total Variation Regularized Deep Image Prior
Jiaming Liu, Yu Sun, Xiaojian Xu +1
In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on anal…