A Variational Model for Joint Motion Estimation and Image Reconstruction
This work addresses motion estimation and image reconstruction for applications like medical imaging or video processing, but it appears incremental as it builds on existing variational frameworks.
The paper tackles the problem of jointly estimating motion and reconstructing image sequences using a variational model based on a time-continuous Eulerian motion model, with rigorous proof of minimizer existence and demonstration of benefits over sequential methods.
The aim of this paper is to derive and analyze a variational model for the joint estimation of motion and reconstruction of image sequences, which is based on a time-continuous Eulerian motion model. The model can be set up in terms of the continuity equation or the brightness constancy equation. The analysis in this paper focuses on the latter for robust motion estimation on sequences of two-dimensional images. We rigorously prove the existence of a minimizer in a suitable function space setting. Moreover, we discuss the numerical solution of the model based on primal-dual algorithms and investigate several examples. Finally, the benefits of our model compared to existing techniques, such as sequential image reconstruction and motion estimation, are shown.