5 citations · 5 across the 2 of their papers we have counts for
6 papers
Fast Data-Driven Learning of MRI Sampling Pattern for Large Scale Problems
Marcelo V. W. Zibetti, Gabor T. Herman, Ravinder R. Regatte
Purpose: A fast data-driven optimization approach, named bias-accelerated subset selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the purpose of…
Fast Proximal Gradient Methods for Nonsmooth Convex Optimization for Tomographic Image Reconstruction
Elias S. Helou, Marcelo V. W. Zibetti, Gabor T. Herman
The Fast Proximal Gradient Method (FPGM) and the Monotone FPGM (MFPGM) for minimization of nonsmooth convex functions are introduced and applied to tomographic image reconstruction…
Problem Structures in the Theory and Practice of Superiorization
Gabor T. Herman
The purpose of this short paper is to identify the mathematical essence of the superiorization methodology. This methodology has been developed in recent years while attempting to…
Derivative-Free Superiorization: Principle and Algorithm
Yair Censor, Edgar Garduño, Elias S. Helou +1
The superiorization methodology is intended to work with input data of constrained minimization problems, that is, a target function and a set of constraints. However, it is based…
The Discrete Fourier Transform for Golden Angle Linogram Sampling
Elias S. Helou, Marcelo V. W. Zibetti, Leon Axel +3
Estimation of the Discrete-Time Fourier Transform (DTFT) at points of a finite domain arises in many imaging applications. A new approach to this task, the Golden Angle Linogram Fo…
String-Averaging Algorithms for Convex Feasibility with Infinitely Many Sets
T. Yung Kong, Homeira Pajoohesh, Gabor T. Herman
Algorithms for convex feasibility find or approximate a point in the intersection of given closed convex sets. Typically there are only finitely many convex sets, but the case of i…