Joint Large-Scale Motion Estimation and Image Reconstruction
This work addresses motion estimation and image reconstruction for applications like video processing, but it appears incremental as it extends an existing framework to large-scale motion.
The paper tackles the problem of improving image reconstruction and motion estimation quality by extending a joint variational framework to handle large-scale motion between frames, using an optical flow approach and total variation regularization, with numerical minimization performed via primal-dual techniques.
This article describes the implementation of the joint motion estimation and image reconstruction framework presented by Burger, Dirks and Schönlieb and extends this framework to large-scale motion between consecutive image frames. The variational framework uses displacements between consecutive frames based on the optical flow approach to improve the image reconstruction quality on the one hand and the motion estimation quality on the other. The energy functional consists of a data-fidelity term with a general operator that connects the input sequence to the solution, it has a total variation term for the image sequence and is connected to the underlying flow using an optical flow term. Additional spatial regularity for the flow is modeled by a total variation regularizer for both components of the flow. The numerical minimization is performed in an alternating manner using primal-dual techniques. The resulting schemes are presented as pseudo-code together with a short numerical evaluation.