Efficient Dynamic Image Reconstruction with motion estimation

TL;DR

This paper introduces a novel regularization method for dynamic image reconstruction that incorporates motion estimation via optical flow, improving computational efficiency and reconstruction quality in large-scale inverse problems.

Contribution

It proposes a new temporal regularization technique using optical flow within a joint optimization framework, solved efficiently with a Krylov subspace method.

Findings

Effective in limited angle tomography scenarios

Improves reconstruction quality with motion-aware regularization

Demonstrates computational efficiency on large-scale problems

Abstract

Dynamic inverse problems are challenging to solve due to the need to identify and incorporate appropriate regularization in both space and time. Moreover, the very large scale nature of such problems in practice presents an enormous computational challenge. In this work, in addition to the use of edge-enhancing regularization of spatial features, we propose a new regularization method that incorporates a temporal model that estimates the motion of objects in time. In particular, we consider the optical flow model that simultaneously estimates the motion and provides an approximation for the desired image, and we incorporate this information into the cost functional as an additional form of temporal regularization. We propose a computationally efficient algorithm to solve the jointly regularized problem that leverages a generalized Krylov subspace method. We illustrate the effectiveness of the prescribed approach on a wide range of numerical experiments, including limited angle and single-shot computerized tomography.