10 citations · 17 across the 6 of their papers we have counts for
7 papers
A Mini-Batch Quasi-Newton Proximal Method for Constrained Total-Variation Nonlinear Image Reconstruction
Tao Hong, Thanh-an Pham, Irad Yavneh +1
Over the years, computational imaging with accurate nonlinear physical models has garnered considerable interest due to its ability to achieve high-quality reconstructions. However…
A Complex Quasi-Newton Proximal Method for Image Reconstruction in Compressed Sensing MRI
Tao Hong, Luis Hernandez-Garcia, Jeffrey A. Fessler
Model-based methods are widely used for reconstruction in compressed sensing (CS) magnetic resonance imaging (MRI), using regularizers to describe the images of interest. The recon…
On Adapting Nesterov's Scheme to Accelerate Iterative Methods for Linear Problems
Tao Hong, Irad Yavneh
Nesterov's well-known scheme for accelerating gradient descent in convex optimization problems is adapted to accelerating stationary iterative solvers for linear systems. Compared…
Solving RED with Weighted Proximal Methods
Tao Hong, Irad Yavneh, Michael Zibulevsky
REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors. A drawback of this a…
Optimized Structured Sparse Sensing Matrices for Compressive Sensing
Tao Hong, Xiao Li, Zhihui Zhu +1
We consider designing a robust structured sparse sensing matrix consisting of a sparse matrix with a few non-zero entries per row and a dense base matrix for capturing signals effi…
Online Learning Sensing Matrix and Sparsifying Dictionary Simultaneously for Compressive Sensing
Tao Hong, Zhihui Zhu
This paper considers the problem of simultaneously learning the Sensing Matrix and Sparsifying Dictionary (SMSD) on a large training dataset. To address the formulated joint learni…
An Efficient Method for Robust Projection Matrix Design
Tao Hong, Zhihui Zhu
Our objective is to efficiently design a robust projection matrix for the Compressive Sensing (CS) systems when applied to the signals that are not exactly sparse. The optimal…