Prediction techniques for dynamic imaging with online primal-dual methods
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915174327255040 |
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| author | Dizon, Neil Jauhiainen, Jyrki Valkonen, Tuomo |
| author_facet | Dizon, Neil Jauhiainen, Jyrki Valkonen, Tuomo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_02497 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction techniques for dynamic imaging with online primal-dual methods Dizon, Neil Jauhiainen, Jyrki Valkonen, Tuomo Optimization and Control Computer Vision and Pattern Recognition 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. |
| title | Prediction techniques for dynamic imaging with online primal-dual methods |
| topic | Optimization and Control Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.02497 |