Prediction techniques for dynamic imaging with online primal-dual methods
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...
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| creator | Dizon, Neil Jauhiainen, Jyrki Valkonen, Tuomo |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2405.02497 |
| format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2405.02497</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Mathematics - Optimization and Control</subject><creationdate>2024-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2405.02497$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2405.02497$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Dizon, Neil</creatorcontrib><creatorcontrib>Jauhiainen, Jyrki</creatorcontrib><creatorcontrib>Valkonen, Tuomo</creatorcontrib><title>Prediction techniques for dynamic imaging with online primal-dual methods</title><description>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.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Mathematics - Optimization and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz71OwzAUBWAvDKjwAEz4BZLG8f-IKgqVKsHQPbqxrxtLiQNO2tK3BwLTkc5wdD5CHlhVCiNltYb8Fc9lLSpZVrWw-pbs3jP66OY4Jjqj61L8POFEw5ipvyYYoqNxgGNMR3qJc0fH1MeE9CP_tH3hT9DTAedu9NMduQnQT3j_nyty2D4fNq_F_u1lt3naF6C0LhCt5ahtcMqBBK5YQBesaLUyqB0zyug2AEjHbMuVMQy080EwVFAH3fIVefybXSzNciRfm19Ts5j4N774SM0</recordid><startdate>20240503</startdate><enddate>20240503</enddate><creator>Dizon, Neil</creator><creator>Jauhiainen, Jyrki</creator><creator>Valkonen, Tuomo</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20240503</creationdate><title>Prediction techniques for dynamic imaging with online primal-dual methods</title><author>Dizon, Neil ; Jauhiainen, Jyrki ; Valkonen, Tuomo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-ee993e79fc6ca5a361fecf94b768e7c18687bfaa5c19b36881a7cdf41e6a2f7b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Mathematics - Optimization and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Dizon, Neil</creatorcontrib><creatorcontrib>Jauhiainen, Jyrki</creatorcontrib><creatorcontrib>Valkonen, Tuomo</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Dizon, Neil</au><au>Jauhiainen, Jyrki</au><au>Valkonen, Tuomo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction techniques for dynamic imaging with online primal-dual methods</atitle><date>2024-05-03</date><risdate>2024</risdate><abstract>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.</abstract><doi>10.48550/arxiv.2405.02497</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition Mathematics - Optimization and Control |
| title | Prediction techniques for dynamic imaging with online primal-dual methods |
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