Prediction techniques for dynamic imaging with online primal-dual methods
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 42 references that are in the map. First posted on arXiv 3 May 2024.
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20 of 42 references.
References · 20
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8 more
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Citing papers per year: 2024: 1.
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