1. Joint Image Reconstruction and Motion Estimation for Spatiotemporal Imaging
- Author
-
Chen, Chong, Gris, Barbara, Öktem, Ozan, Academy of Mathematics and Systems Science (AMSS), Chinese Academy of Sciences [Beijing] (CAS), Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Control And GEometry (CaGE ), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Department of Mathematics (KTH Royal Institute of Technology), Royal Institute of Technology [Stockholm] (KTH ), and Gris, Barbara
- Subjects
Large diffeomorphic deformations ,Image reconstruction ,AMS subject classifications.65F22, 65R32, 65R30, 65D18, 65J22, 65J20, 65L09, 68U10, 94A12, 94A08, 92C55, 54C56, 57N25, 47A52 ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Joint variational model ,[MATH] Mathematics [math] ,[MATH]Mathematics [math] ,Motion estimation ,Shape theory ,Spatiotemporal imaging - Abstract
International audience; We propose a variational model for joint image reconstruction and motion estimation applicable to spatiotemporal imaging. This model consists of two parts, one that conducts image reconstruction in a static setting and another that estimates the motion by solving a sequence of coupled indirect image registration problems, each formulated within the large deformation diffeomorphic metric mapping framework. The proposed model is compared against alternative approaches (optical flow based model and diffeomorphic motion models). Next, we derive efficient algorithms for a time-discretized setting and show that the optimal solution of the time-discretized formulation is consistent with that of the time-continuous one. The complexity of the algorithm is characterized and we conclude by giving some numerical examples in 2D space + time tomography with very sparse and/or highly noisy data
- Published
- 2019