Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction

DOI10.1088/1361-6420/33/4/044010zbMath1367.65029arXiv1608.04952OpenAlexW3098116618MaRDI QIDQ5346629

Eduardo X. Miqueles, Elias Salomão Helou, Marcelo V. W. Zibetti

Publication date: 26 May 2017

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.04952

zbMATH Keywords

convex optimizationsuperiorizationtomographic image reconstructionstring averaging expectation maximum

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Computing methodologies for image processing (68U10) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (3)

Strong convergence and bounded perturbation resilience of a modified proximal gradient algorithm ⋮ Total variation superiorized conjugate gradient method for image reconstruction ⋮ Perturbation resilience of proximal gradient algorithm for composite objectives

Cites Work

• Convergence results for scaled gradient algorithms in positron emission tomography
• A Statistical Model for Positron Emission Tomography
• Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems

This page was built for publication: Superiorization of incremental optimization algorithms for statistical tomographic image reconstruction