Prediction techniques for dynamic imaging with online primal-dual methods
This work addresses dynamic inverse problems in imaging for applications such as medical imaging and fluid monitoring, but it is incremental as it builds on previous methods.
The paper tackled dynamic imaging problems like image stabilization and dynamic PET by improving predictive online primal-dual methods, resulting in more concise analysis and new dual predictors that demonstrated efficacy in numerical experiments.
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.