Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Circuits and Systems for Video Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
