Compressive Sensing Reconstruction for Video: An Adaptive Approach Based on Motion Estimation

This paper focuses on the problem of causally reconstructing compressive sensing (CS) captured video. The state-of-the-art causal approaches usually assume that the signal support is static or changing sufficiently slowly over time, where magnetic resonance imaging is widely used as a motivating example. However, such an assumption is too restrictive for many other video applications, where the signal support changes rapidly. In this paper, we propose a framework that combines motion estimation (ME), the Kalman filter (KF), and CS to adapt the reconstruction process to motions in the video so that the slowly changing assumption on the signal support is relaxed and consequently is more suitable for video reconstruction. Explicit and implicit ME are designed to provide motion-aware predictions, upon which a modified KF procedure is applied. Furthermore, three CS algorithms with embedded ME and KF are developed, and theoretical analyses are conducted via reconstruction error upper bounds to characterize the various factors that affect reconstruction accuracy. Extensive simulations utilizing actual videos are carried out, and the superiority of our methods is demonstrated.