Search Results for author:
Found 11 papers, 3 papers with code
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior
Phase retrieval (PR) is a crucial problem in many imaging applications.
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters.
Online Deep Equilibrium Learning for Regularization by Denoising
However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.
Monotonically Convergent Regularization by Denoising
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.
Bregman Plug-and-Play Priors
Our new Bregman Proximal Gradient Method variant of PnP (PnP-BPGM) and Bregman Steepest Descent variant of RED (RED-BSD) replace the traditional updates in PnP and RED from the quadratic norms to more general Bregman distance.
Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping
LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
Scalable Plug-and-Play ADMM with Convergence Guarantees
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.
Image Restoration using Total Variation Regularized Deep Image Prior
In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.
signProx: One-Bit Proximal Algorithm for Nonconvex Stochastic Optimization
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for parallel and distributed processing of large datasets.
