Prediction Techniques for Dynamic Imaging with Online Primal-Dual Methods

被引：0
作者：
D. Dizon, Neil [1 ]
Jauhiainen, Jyrki [2 ]
Valkonen, Tuomo [1 ,3 ]
机构：
[1] Univ Helsinki, Dept Math & Stat, Helsinki, Finland
[2] Univ Eastern Finland, Dept Tech Phys, Kuopio, Finland
[3] Res Ctr Math Modeling MODEMAT, Quito, Ecuador
来源：
JOURNAL OF MATHEMATICAL IMAGING AND VISION | 2024年 / 66卷 / 06期
基金：
芬兰科学院;
关键词：
Online; Primal-dual; Optimization; Dynamic imaging; ALGORITHMS;
D O I：
10.1007/s10851-024-01214-w
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Online optimisation facilitates the solution of dynamic inverse problems, such as image stabilisation, fluid flow monitoring, and dynamic medical imaging. In this paper, we improve upon previous work on predictive online primal-dual methods on two fronts. Firstly, we provide a more concise analysis that symmetrises previously unsymmetric regret bounds, and relaxes previous restrictive conditions on the dual predictor. Secondly, based on the latter, we develop several improved dual predictors. We numerically demonstrate their efficacy in image stabilisation and dynamic positron emission tomography.
引用
收藏
页码：1109 / 1134
页数：26
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