A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging

TL;DR

This paper introduces a novel variational model for simultaneous image reconstruction and motion estimation in spatiotemporal imaging, combining shape theory and large deformation diffeomorphic mapping, with theoretical analysis and efficient algorithms demonstrated on noisy, sparse data.

Contribution

It presents a new joint variational framework integrating static image reconstruction and sequential motion estimation using generalized diffeomorphic registration, with proven properties and efficient algorithms.

Findings

The model has desirable optimality properties.

Algorithms are consistent between time-discrete and continuous cases.

Numerical examples show effectiveness on sparse, noisy data.

Abstract

We propose a new variational model for joint image reconstruction and motion estimation in spatiotemporal imaging, which is investigated along a general framework that we present with shape theory. This model consists of two components, one for conducting modified static image reconstruction, and the other performs sequentially indirect image registration. For the latter, we generalize the large deformation diffeomorphic metric mapping framework into the sequentially indirect registration setting. The proposed model is compared theoretically against alternative approaches (optical flow based model and diffeomorphic motion models), and we demonstrate that the proposed model has desirable properties in terms of the optimal solution. The theoretical derivations and efficient algorithms are also presented for a time-discretized scenario of the proposed model, which show that the optimal solution of the time-discretized version is consistent with that of the time-continuous one, and most of the computational components is the easy-implemented linearized deformation. The complexity of the algorithm is analyzed as well. This work is concluded by some numerical examples in 2D space + time tomography with very sparse and/or highly noisy data.